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.
2024
Abstract
Forest management planning often relies on Airborne Laser Scanning (ALS)-based Forest Management Inventories (FMIs) for sustainable and efficient decision-making. Employing the area-based (ABA) approach, these inventories estimate forest characteristics for grid cell areas (pixels), which are then usually summarized at the stand level. Using the ALS-based high-resolution Norwegian Forest Resource Maps (16 m × 16 m pixel resolution) alongside with stand-level growth and yield models, this study explores the impact of three levels of pixel aggregation (stand-level, stand-level with species strata, and pixel-level) on projected stand development. The results indicate significant differences in the projected outputs based on the aggregation level. Notably, the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation, ranging from −301 to +253 m3⋅ha−1 for single stands. The differences were, on average, higher for broadleaves than for spruce and pine dominated stands, and for mixed stands and stands with higher variability than for pure and homogenous stands. In conclusion, this research underscores the critical role of input data resolution in forest planning and management, emphasizing the need for improved data collection practices to ensure sustainable forest management.
Abstract
Mapping individual tree quality parameters from high-density LiDAR point clouds is an important step towards improved forest inventories. We present a novel machine learning-based workflow that uses individual tree point clouds from drone laser scanning to predict wood quality indicators in standing trees. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information on point distances, angles, and geometric attributes of the space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting the diameter of the largest branch per log (DLBs) and stem diameter at different heights (DS) from survey-grade drone laser scans. We show that our models are also robust and accurate when tested on suboptimal versions of the data generated by reductions in the number of points or emulations of suboptimal single-tree segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations both for research and more operational mapping.
Authors
Eivind Handegard Ivar Gjerde Rune Halvorsen Robert John Lewis Ken Olaf Storaunet Magne Sætersdal Olav SkarpaasAbstract
Multiple ecological drivers, along with forest age, determine the species composition of boreal forest ecosystems. However, the role of age in successional changes in forests cannot be understood without taking site conditions, the disturbance regime and forest structure into account. In this study, we ask two research questions: 1. What is the relationship between forest age and overall species composition in older near-natural spruce forests, i.e. forests of age beyond harvest maturity? 2. Do species associated with different forest habitats respond similarly to variation in forest age? Data were collected in 257 Norway spruce dominated 0.25 ha plots from three study areas in Southeastern and Central Norway. Species inventories were conducted for lichens and bryophytes on trees and rocks, vascular plants on the forest floor, and for deadwood-associated bryophytes and polypore fungi. Although NMDS ordination analyses of the total species composition identified a main axis related to the age of the oldest trees in two of the study areas, variation partitioning analyses showed that age explained a small fraction of variation of the species composition compared to site conditions, logging history, forest structure, and differences between the sites in all habitats. The unique variation explained by forest age species was, however, significant for all habitats. The fraction of variation in species composition explained by forest age was the largest for lichens and bryophytes on trees, and for deadwood-associated bryophytes and polypore fungi. Our results suggest that practical mapping of near-natural forests for management purposes inventories should include site conditions, forest structure and between site differences in addition to forest age.
Abstract
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Authors
Christian Wilhelm Mohr Johannes Breidenbach Gunnhild Søgaard Oliver Moen Snoksrud Rune EriksenAbstract
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Authors
Stephen Amiandamhen Synne Strømmen Ingeborg Olsdatter Ohren Nordraak Andreas Treu Erik LarnøyAbstract
This study investigated the potential of wood particles from Ciol®-treated wood in particleboard production. Ciol® is a renewable formulation from water, citric acid, and sorbitol, which has been commercially developed as a promising alternative for wood modification. Radiata pine wood was impregnated with 60% and 85% concentrations of the Ciol® solution for 150 mins. The impregnated boards were cured and subsequently planned. Particleboards were thereafter produced from the wood shavings using urea formaldehyde (UF) and melamine urea formaldehyde resin (MUF). The boards were produced with or without the use of ammonium nitrate as a hardener. The wood particles and produced boards were characterized via analytical techniques and standard test methods. The effect of Ciol® treatment and its concentration on the properties of the shavings and the particleboards was investigated as well as the effect of the resin type on the panel properties. The use of MUF without the hardener gave the best bending strength of 13 N/mm² and modulus of elasticity of 3187 N/mm². However, there was no significant difference in the results obtained when the hardener was added to MUF resins. Recycling Ciol®-treated wood shavings in particleboard production proved to be a promising approach with MUF resins.
Authors
Binbin Xiang Maciej Wielgosz Theodora Kontogianni Torben Peters Stefano Puliti Rasmus Astrup Konrad SchindlerAbstract
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but automatically partitioning those point clouds into meaningful entities like individual trees or tree components remains a challenge. The present study aims to fill this gap and introduces a deep learning framework, termed ForAINet, that is able to perform such a segmentation across diverse forest types and geographic regions. From the segmented data, we then derive relevant biophysical parameters of individual trees as well as stands. The system has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones. The segmentation back-end achieves over 85% F-score for individual trees, respectively over 73% mean IoU across five semantic categories: ground, low vegetation, stems, live branches and dead branches. Building on the segmentation results our pipeline then densely calculates biophysical features of each individual tree (height, crown diameter, crown volume, DBH, and location) and properties per stand (digital terrain model and stand density). Especially crown-related features are in most cases retrieved with high accuracy, whereas the estimates for DBH and location are less reliable, due to the airborne scanning setup.
Abstract
This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis. Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers. Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods. In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.
Abstract
There is currently no quality sorting of harvested hardwood timber in Norway on a national scale. Medium- and high-quality logs including those from birch (Betula pubescens Ehrh., B. pendula Roth) are thus not utilized according to their potential monetary value. Increased domestic utilization of quality birch timber requires that the quality of harvested logs be properly assessed for potential end uses. A preferred sorting procedure would use visually detectable external log defects to grade roundwood timber. Knots are an important feature of inner log quality. Thus, the aim of this study was to evaluate whether correlations between branch scar size and knot features could be found in Norwegian birch. Using 168 knots from seven unpruned birch trees, external bark attributes often showed strong correlations with internal wood quality. Both length of the mustache and length of the seal performed well as predictors of stem radius at the time of knot occlusion. The presence of a broken off branch stub as part of an occluded knot significantly increased the knot-effected stem radius, proving that the practice of removing branches and branch stubs along the lower trunk is a crucial measure if quality timber production is the primary management goal.