## 4 Discussion
This approach considers the terrain slope and shadow effects on satellite visibility, providing a more comprehensive and accurate analysis of the canopy cover model. Similarly, a stronger relationship was observed for the viewshed analysis of the DSM in relation to PDOP. Despite the greater computational demands of the viewshed analysis of the DSM, these findings suggest that it provides more precise results when evaluating the impact of canopy cover on the harvester GNSS's positioning. The results of this study are comparable with a similar study [10], where the determination coefficient (R\({}^{2}\)) in the regression analysis for a coniferous forest model was 0.579. This indicated that the model could predict the maximum positioning error under different canopy covers. In our study, 86% of observations had a maximum horizontal precision of 8 mm, a percentage which obviously may vary in different forest environments. Integrating the GNSS with LiDAR could improve the accuracy by compensating for the errors introduced by the canopy cover. Advanced GNSS technologies can benefit also from integration with GNSS-aided inertial navigation systems. Continuous cover forestry is expected to decrease the impact of canopy cover on the GNSS's accuracy. This study has various limitations. For instance, some trees might have been harvested before collecting the canopy cover data, which could have underestimated the impact of canopy cover on the observed GNSS signals. The terms accuracy and precision were used, but it should be noted that the precise positions were not necessarily accurate. The study focused on PDOP and assumed that this was directly related to accuracy, but we also acknowledge that this was not necessarily true. It was possible to obtain precise clusters of positions that were inaccurately located. In forest environments, there were challenges with GNSS's positioning due to signals from multiple satellites reaching the receiver via indirect paths, resulting in long periods of signal interruptions and reflections. Furthermore, the boom on the harvester could have potentially caused problems for the GNSS signals as it passed close to the receiver, or when a tree held in the processor head passed close to the receiver. This could have caused signal interruptions and/or reflections for short durations, which were often not filtered out by the receiver, leading to position errors. The models to predict PDOP developed within this study have limitations as they were forced to zero intercept due to the reason that there were no points available within open-sky conditions. The minimum canopy cover observation from the canopy height model was 0.54%, while the minimum canopy cover evaluated by the viewshed analysis of the DSM was 4.03%. When looking at the observed minimum for PDOP, it was found to be approximately 1.2 for accurate points and 1.4 for inaccurate points. It is possible that even lower values for PDOP could be expected in open-sky conditions, but, unfortunately, within this study, there were no points available without the impact of canopy cover. Future studies should investigate this further. This study also has a limitation in terms of the radius used to assess the impact of the canopy on GNSS signals. The 10 m radius was probably considered too small to capture the full extent of the impact. The trees in the area had an average height of 22 m with a radius of 10 m, and the GNSS's antenna was 4 m above the ground; satellites below about 62 degrees were not taken into consideration. This could have potentially led to an underestimation of the amount of canopy that impacts signals from several satellites that were below this angle. Future studies should repeat the test without harvesting and compare the results before and after harvesting. Future research on the impact of canopy cover on harvester positioning under the canopy should consider the following investigations:  
1. Conduct long-term field studies to monitor the GNSS's accuracy over time under different weather and foliage conditions;
2.