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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

RoadSens is a platform designed to expedite the digitalization process of forest roads, a cornerstone of efficient forest operations and management. We incorporate stereo-vision spatial mapping and deep-learning image segmentation to extract, measure, and analyze various geometric features of the roads. The features are precisely georeferenced by fusing post-processing results of an integrated global navigation satellite system (GNSS) module and odometric localization data obtained from the stereo camera. The first version of RoadSens, RSv1, provides measurements of longitudinal slope, horizontal/vertical radius of curvature and various cross-sectional parameters, e.g., visible road width, centerline/midpoint positions, left and right sidefall slopes, and the depth and distance of visible ditches from the road’s edges. The potential of RSv1 is demonstrated and validated through its application to two road segments in southern Norway. The results highlight a promising performance. The trained image segmentation model detects the road surface with the precision and recall values of 96.8 and 81.9 , respectively. The measurements of visible road width indicate sub-decimeter level inter-consistency and 0.38 m median accuracy. The cross-section profiles over the road surface show 0.87 correlation and 9.8 cm root mean squared error (RMSE) against ground truth. The RSv1’s georeferenced road midpoints exhibit an overall accuracy of 21.6 cm in horizontal direction. The GNSS height measurements, which are used to derive longitudinal slope and vertical curvature exhibit an average error of 5.7 cm compared to ground truth. The study also identifies and discusses the limitations and issues of RSv1, which provide useful insights into the challenges in future versions.

2024

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.

To document

Abstract

Uneven-aged forests set certain challenges for cut-to-length harvesting work. It is a challenge to cost-effectively remove larger trees while leaving a healthy understory for regrowth. The study’s aim was to evaluate productivity and costs of harvesting two-storied silver birch (Betula pendula Roth) and Norway spruce (Picea abies (L.) H. Karst.) stands by creating time consumption models for cutting, and using existing models for forwarding. Damage to the remaining understory spruce was also examined. Four different harvesting methods were used: 1) all dominant birches were cut; 2) half of them thinned and understory was preserved; compared to 3) normal thinning of birch stand without understory; and 4) clear cutting of two-storied stand. Results showed the time needed for birch cutting as 26–30% lower when the understory was not preserved. Pulpwood harvesting of small sized spruces that prevent birch cutting was expensive, especially because of forwarding of small amounts with low timber density on the strip roads. Generally, when taking the cutting and forwarding into account, the unit cost at clear cuttings was lowest, due to lesser limitations on work. It was noted that with increasing removal from 100 to 300 m3 ha–1, the relative share of initial undamaged spruces after the harvest decreased from 65 to 50% when the aim was to preserve them. During summertime harvesting, the amount of stem damage was bigger than during winter. In conclusion, two-storied stands are possible to transit to spruce stands by accepting some losses in harvesting productivity and damages on remaining trees.

To document

Abstract

Eucalyptus plantations are a notable source of income for smallholders and private landowners in Thailand. The main uses of eucalyptus are for energy purposes and as pulpwood, sawn timber, and veneer. Among private eucalyptus forest owners there is a need for decision support tools that can help in optimizing tree bucking, according to the available properties of the site and bucking patterns. The precise characterization of plantation properties is key to delivering appropriate timber assortment to markets and optimizing timber value. Our study has developed and tested dynamic and linear programming models for optimizing the tree bucking of eucalyptus trees. To achieve this, tree taper curves for use in volumetric models were defined for optimization. Our results indicate that both the tree spacing and the increment of diameter of breast height are significant factors when estimating profitability. The income would be significantly higher if bucking timber in different assortments were used, instead of the current approach of selling as bulk based on mass. For implementation, we created a free mobile application for android phones (EVO—eucalyptus value chain optimization) to utilize the study results at the grass root-level.