###### Abstract  
This study evaluated the positioning accuracy of moving forest harvesters using global navigation satellite system (GNSS) signals under a forest canopy, and developed approaches for forecasting accuracy under a mature spruce canopy cover. Real-time kinematic positioning with a Trimble R12 receiver on top of a harvester achieved high positioning accuracy, with 86% of observations meeting a maximum precision of 8 mm. However, the presence of a canopy cover hampered the GNSS's performance as there were fewer satellites available, leading to an increased number of inaccurate positions and larger values of the dilution of precision in geometry (GDOP), position (PDOP), vertical (VDOP) and horizontal directions (HDOP). The canopy cover estimated from the viewshed analysis of the digital surface model (DSM) was found to be a significant predictor of the dilution of precision and maximum deviation from the true position. These findings suggest that viewshed analysis provides more precise results than a mere canopy cover percentage for evaluating the impact of canopy cover on the GNSS's positioning of a harvester, despite its computational demands. Developing intelligent algorithms for precise positioning under the canopy can facilitate autonomous harvesting and forwarding, allowing for the implementation of digitalization in forest operations. digitalization; forest operations; viewshed analysis; GNSS positioning accuracy; harvester, canopy cover; forecasting accuracy; autonomous harvesting +
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individual trees and the relationships among trees, forests, and their environment. This information can help those involved in the forestry sector to make better models and decisions that support the environment, the economy, and society. Many harvesters are equipped with GNSS devices, allowing the data to be georeferenced. However, the accuracy of this georeferencing is often uncertain, and implementations are often sub-optimal; it can be influenced by factors such as canopy cover which breaks the line of sight to navigation satellites. There is a need to establish a method for evaluating the harvester GNSS's positioning, especially under a mature canopy cover, so that the information of the canopy cover can be used to predict the expectation for the GNSS's positional accuracy. Conventional even-aged rotational forest management concludes in clear cutting and forest regeneration by natural, sowing, or planting [13; 14; 15]. Continuous cover forestry is a silvicultural alternative aimed at achieving multifunctional objectives including increased species diversity, a more resilient stand structure, and increased carbon storage [16; 17; 18; 19; 20; 21]. Improving the accuracy of under-canopy harvester positioning is a necessity to increase the efficiency and practicality of this approach, including the development of new harvesting methods based on the precise positioning, tree selection, and collection of stand structure data before and after harvesting. An accurate harvester positioning under the canopy can lead to several benefits: an improved efficiency, better harvest quality and improved forest growth potential, enhanced safety, reduced negative environmental impact and waste, and better inventory management. Recent studies which utilized low-cost mobile devices [11] were conducted in stands without GNSS devices on the harvesters [4; 5; 6; 7; 9; 10; 11]. Upgrading GNSS receivers with advanced technologies such as real-time kinematic (RTK) or differential GNSSs can improve accuracy. Using multiple GNSS constellations, such as global positioning system (GPS), globalnaja navigatsionnaja sputnikovaja sistema (GLONASS), and Galileo, can also increase accuracy by providing additional signals from different directions.