Hopp til hovedinnholdet

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

To document

Abstract

Background: Small-scale forests (woodlots) increasingly account for a greater proportion of the total annual harvest in New Zealand. There is limited information on the extent of infrastructure required to harvest a woodlot; road density (trafficable with log trucks), landing size, or the average harvest area that each landing typically services. Methods: This study quantified woodlot infrastructure averages and evaluated influencing factors. Using publicly available aerial imagery, roads and landings were mapped for a sample of 96 woodlots distributed across the country. Factors such as total harvest area, average terrain slope, length/width ratio, boundary complexity and extraction method were recorded and investigated for correlations. Results: The average road density was 25 m/ha, landing size was 3000 m2 and each landing was serviced on average 12.8 ha. Notably, 15 of the 96 woodlots had no internal infrastructure, with the harvest completed using roads and landings located outside of the woodlot boundary. Factors influencing road density were woodlot length/width ratio, average terrain slope and boundary complexity. Landing size was influenced by average terrain slope, woodlot length/width ratio, and woodlot area. Conclusion: The results provide a contemporary benchmark of the current infrastructure requirements when harvesting a small-scale forests in New Zealand. These may be used at a high level to infer the total annual infrastructure investment in New Zealand's woodlot estate and also project infrastructure requirements over the foreseeable future. Keywords: forest infrastucture, small-scale forestry

To document

Abstract

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.

To document

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.

To document

Abstract

No abstract has been registered

To document

Abstract

Invasions are one of main drivers transforming the functions of forest ecosystems. The inva­sion of alien fungus Hymenoscyphus fraxineus is still reducing the abundance of Fraxinus excelsior throughout temperate Europe. F. excelsior is a tree species belonging to the group of foundation species for numerous epiphytic species. We studied the effects of F. excelsior decline on epiphytic bryophytes in the Białowieża Primeval Forest. In this forest human interference is limited, allowing us to register the natural dynamics of ash-dependent bryophyte communities. F. excelsior decline was discovered in the Białowieża Primeval Forest in 1998, and in 2016 we resurveyed a historical survey of epiphytic bryophytes, i.e. shortly before the dieback process started. Using ordination methods and mixed-effect models we assessed shifts in ep­iphyte bryophytes composition over time and amongst the plots with (i) historical and recent presence of F. excelsior, (ii) with recent extinction of F. excelsior, and (iii) absence of F. excelsior both historically and recently, as well as at the level of alternative tree hosts employing the paired Mann-Whitney and t-tests. F. excelsior dieback did not influence the species composition of bryophytes associated with this tree host. Despite the drastic reduction in living F. excelsior trees (85%), overall the species composition, species rich­ness and Shannon index of F. excelsior-dwelling bryophytes did not shift significantly between two sampling periods. Similarly weak changes over time we reported for the bryophytes’ community weighted means of ecological indicator values. Equally subtle temporal shifts in epiphytes’ biodiversity were observed amongst the plots with the presence, absence, and extinction of ash, likely due to the relatively high diversity of available alternative hosts. F. excelsior-associated epiphytic bryophytes were able to exploit other niches in the microhabitat-heterogeneous Białowieża Forest ecosystem, and thus far have not suffered a reduction in biodiversity parameters at the scale of our survey. High diversity of alternative host tree species, with particular emphasis on the occurrence of pioneer trees (i.e. B. pendula and P. tremula), may maintain the epiphytic bryophyte communities, which themselves may be able to act as a source for the recovery of F. excelsior-affiliated epiphyte populations.

To document

Abstract

Biological nitrogen fixation is a fundamental part of ecosystem functioning. Anthropogenic nitrogen deposition and climate change may, however, limit the competitive advantage of nitrogen-fixing plants, leading to reduced relative diversity of nitrogen-fixing plants. Yet, assessments of changes of nitrogen-fixing plant long-term community diversity are rare. Here, we examine temporal trends in the diversity of nitrogen-fixing plants and their relationships with anthropogenic nitrogen deposition while accounting for changes in temperature and aridity. We used forest-floor vegetation resurveys of temperate forests in Europe and the United States spanning multiple decades. Nitrogen-fixer richness declined as nitrogen deposition increased over time but did not respond to changes in climate. Phylogenetic diversity also declined, as distinct lineages of N-fixers were lost between surveys, but the “winners” and “losers” among nitrogen-fixing lineages varied among study sites, suggesting that losses are context dependent. Anthropogenic nitrogen deposition reduces nitrogen-fixing plant diversity in ways that may strongly affect natural nitrogen fixation.