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
No abstract has been registered
Authors
Jian Liu Faruk Djodjic Barbro Ulén Helena Aronsson Marianne Bechmann Lars Bergström Tore Krogstad Katarina KyllmarAbstract
No abstract has been registered
2024
Authors
Mostafa Hoseini Helle Ross Gobakken Stephan Hoffmann Csongor Horvath Johannes Rahlf Jan Bjerketvedt Stefano Puliti Rasmus AstrupAbstract
No abstract has been registered
Abstract
Large clear-cut areas as a consequence of drought and bark beetle infestations necessitate extensive replanting efforts in German forests, leading to an increased interest in efficient planting systems. In addition to manual planting, mechanized and semi-mechanized systems utilizing surplus forest machine capacities available after completion of salvage logging operations are likely required for timely reforestation of the clear-cut areas. A semi-mechanized system utilizing a standard forwarder with a grapple-actuated soil borer for both, the transport of planting material and the preparation of planting pits, combined with two workers carrying out manual planting, was investigated in a time-and-motion study. The frequency method was used after video recording of a planting operation that covered an area of approximately 1.2 hectares. A total of 815 alder saplings (Alnus glutinosa L.) with heights of 1.2–1.5 m were planted. Observed productivity was 93 saplings per system work hour (SWH). With additional placement of stakes for stabilizing the plants, the productivity decreased to 42 saplings per SWH. While directly comparable results were not found in the literature, available productivity figures of purely manual planting systems do not suggest an increased productivity of this semi-mechanized system. Considering ergonomics, however, forwarder utilization provides reduced workload not only in plant hole preparation but also with material transport and clearing of planting spots. Both the ergonomic aspects of the system and, in particular, the suitability of the soil borer for different soil textures should be further investigated.
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
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Helle Ross Gobakken Mostafa Hoseini Stephan Hoffmann Jan Bjerketvedt Johannes Rahlf Rasmus AstrupAbstract
No abstract has been registered
Abstract
Point clouds from laser scanning are a standard data source in forest research, but the lack of clear and robust workflows has limited the translation of experimental knowledge into operational tools. We present a novel machine learning-based workflow that uses individual-tree point clouds from drone laser scanning to predict wood quality parameters. Unlike object reconstruction methods, our approach is based on simple metrics computed on vertical slices that summarize information of point distances, angles, and geometric attributes of the empty space between and around the points. Our models use these slice metrics as predictors and achieve high accuracy for predicting diameter of the largest branch per whorl (DLBw) and stem diameter at different heights (DSi) 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 segmentation scenarios. Our approach provides a simple, clear, and scalable solution that can be adapted to different situations and has the potential to revolutionize forest management and monitoring
Abstract
Climate change poses significant challenges to forests, increasing the occurrence and severity of disturbances such as windthrows, wildfires, pests, and diseases. Effective decision-making tools are crucial for mitigating forest damages and enhancing resilience under changing climatic conditions. We explore the role of forest decision support systems (DSS) in addressing these challenges and present their potential in mitigating future climate-induced damages to forests. We examine the key challenges associated with integrating disturbances and their interactions into decision support systems, using as motivating example the decision support systems for the high-resolution Norwegian forest resources map SR16.