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
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No abstract has been registered
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Pollarding in agroforestry systems was traditionally an important practice for fodder acquisition in Western Norway, as well as in many other parts of the world. The practice has long been in decline, but to maintain cultural landscapes and biodiversity enhancement from pollarding, farmers now receive a public grant for each tree they pollard. In this interdisciplinary study we investigate which ecosystem services modern pollarding practices provide, under the influence of the current pollarding policy. We have performed both in-depth interviews and a quantitative survey targeting all pollarding farmers in the county of Vestland in Western Norway. We find that bioresources obtained from the branches from pollarding are to some extent still taken into use, mainly in the form of tree fodder for farm animals and firewood, but a lot of the branches remain unused. Biodiversity benefits are obtained from preserving old trees that often are located on agricultural land as solitary trees, as these trees provide important habitats, particularly for species growing on the bark, such as lichens and mosses, or within the decaying wood, such as, for example, fungi and insects. The modern practice of letting branches rot in the field provide habitats for insects and hence additional benefits to biodiversity. For the farmers, the main motivations to pollard are the cultural, aesthetic and historical values of pollarded trees. They see few disadvantages with pollarding, and most of them plan to continue in the future. The grant provides an incentive for pollarding, but our results indicate that the practice would continue without it, although less than now, especially with the establishment of new pollards.
2023
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Sustainable forest management systems require operational measures to preserve the functional design of forest roads. Frequent road data collection and analysis are essential to support target-oriented and efficient maintenance planning and operations. This study demonstrates an automated solution for monitoring forest road surface deterioration using consumer-grade optical sensors. A YOLOv5 model with StrongSORT tracking was adapted and trained to detect and track potholes in the videos captured by vehicle-mounted cameras. For model training, datasets recorded in diverse geographical regions under different weather conditions were used. The model shows a detection and tracking performance of up to a precision and recall level of 0.79 and 0.58, respectively, with 0.70 mean average precision at an intersection over union (IoU) of at least 0.5. We applied the trained model to a forest road in southern Norway, recorded with a Global Navigation Satellite System (GNSS)−fitted dashcam. GNSS-delivered geographical coordinates at 10 Hz rate were used to geolocate the detected potholes. The geolocation performance over this exemple road stretch of 1 km exhibited a root mean square deviation of about 9.7 m compared to OpenStreetMap. Finally, an exemple road deterioration map was compiled, which can be used for scheduling road maintenance operations.
Abstract
Butt rot is a main defect in Norway spruce (Picea abies (L.) Karst.) trees and causes large economic losses for forest owners. However, little empirical research has been done on the effects of butt rot on harvested roundwood and the magnitude of the resulting economic losses. The main objective of this study was to characterize the direct economic losses caused by butt rot in Norway spruce trees for Norwegian forest owners. We used data obtained from seven cut-to-length harvesters, comprising ∼400,000 trees (∼140,000 m3) with corresponding stem profiles and wood grade information. We quantified the economic losses due to butt rot using bucking simulations, for which in a first case, defects caused by butt rot were included, and in a second case, all trees were assumed to be free of butt rot. 16% of trees were affected by butt rot, whereby butt rot tended to occur in larger trees. When butt rot was present in a tree, the saw log volume was reduced by 48%. Proportions of roundwood volume affected by butt rot varied considerably across harvested stands. Our results suggest that butt rot causes economic losses upwards of 7% of wood revenues, corresponding to € 18.5 million annually in Norway.
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The identification of individual tree logs along the wood procurement chain is a coveted goal within the forest industry. The tracing of logs from the sawmill back to the forest would support the legal and sustainable sourcing of wood, as well as increase the resource efficiency and value of harvested timber. In this work, using a dataset of thousands of Scots pine (Pinus sylvestris L.) log end images displaying varying perspectives, lighting, and aging effects, we develop and assess log identification methods based on deep convolutional neural networks. The estimated rank-1 accuracy of our final model on an independent test set of 99 logs is 84 and 91% when allowing for random rotations of the log ends and when keeping each log at approximately fixed orientation, respectively. We estimate the scaling of these methods up to a template pool size of 493 logs, which reveals a weak dependence of accuracy on pool size for logs at fixed orientation. The deep learning approach gives superior results to a classical local binary pattern method, and appears feasible in practice, assuming that pre-filtering of the log database can be leveraged depending on the use case and properties of the queried log image. We make our dataset publicly available.