## 5 Discussion and Conclusions  
The aim of this study was to investigate the positioning accuracy of novel GNSS devices under forest canopies representing varying forest conditions. The study was carried out to simulate the accuracy that could be obtained when using a harvester. The acquisition of detailed and spatially accurate tree data is required in forest inventories based on remote sensing for ground-based truth, and a harvester in motion is one potential data source for such data [4; 9; 15; 39; 40]. Based on the results, the required accuracy level of 1 m for tree matching cannot be achieved when using stand-alone GNSS devices, but when differential GNSS (real-time or post-processed) together with IMU data are applied, accuracy of even less than 1 m can be achieved, e.g., in this study this was 0.6-0.8 m. Thus, in theory, if high-precision GNSS + IMU units are mounted onto a harvester, then the collected stem data can be linked to remote-sensing data and further utilized in forest-inventory applications. However, the fusion of harvester data and remote-sensing data warrants further research. The present study addressed only the positional problem studied using an ATV instead of a harvester. In practice, unlike the ATV in this study, the harvester is not constantly on the move, and the satellite antenna is located higher above the ground [41], and consequently improved accuracy in real-life scenario can be expected under similar forest conditions. On the other hand, satellite geometry is continually changing, and (even though the test data were collected at different times of the day) positioning accuracy may improve or deteriorate from what is reported here as \"satellite conditions\" change. The remote sensing part of the method has been studied by [PERSON] _et al._[16], who looked into the prediction of basic stand variables, such as stem volume, mean tree height, diameter, and stem density, and used manually taken measurements to match the logged trees with the ALS data since the harvesters cannot provide the needed positioning. The RMSE of real-time 2D GNSS positioning for single-frequency receivers varied between 4 and 9 m in mature and thinning stands. This level of accuracy is not adequate to enable the accurate determination of the real-time position of a moving vehicle within the forest. However, the accuracy improves when the harvester remains stationary for longer periods. It seems that the accuracy of novel single-frequency GNSS devices is not so dependent on the forest conditions, whereas the performance of the tested geodetic dual-frequency receiver appears to be more sensitive to satellite visibility. In situations where \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{3}{c}{**RMSE (m), Mean of Two Runs (No. of Observations)**} & \multicolumn{2}{c}{**ANOVA**} \\ \cline{2-7}  & **Young Pine** & **Young Spruce** & **Mature Pine** & **Mature Spruce** & **F** & _p-Value_ \\ \hline Garmin eTrex 30 & 9.3 (277) & 8.5 (249) & 8.2 (709) & 7.7 (278) & 20.0 & \textless{}0.001 \\ u-blox MAX-M5Q & 4.2 (277) & 4.8 (249) & 4.7 (709) & 4.6 (278) & 10.0 & \textless{}0.001 \\ NVS single point & 5.5 (123) & 6.8 (76) & 6.1 (336) & 6.8 (118) & 3.00 & 0.030 \\ NVS post-proc. GPS only & 5.9 (124) & 7.7 (84) & 6.6 (338) & 6.9 (114) & 2.85 & 0.036 \\ NovAtel single point & 7.6 (272) & 31.4 (229) & 27.2 (672) & 49.3 (249) & 4.01 & 0.007 \\ NovAtel post-proc. & 0.8 (249) & 1.3 (145) & 1.9 (524) & 2.7 (123) & 71.63 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The positioning accuracy under different forest stand conditions and the resultant ANOVA values. post-processing and dual-frequency satellite data can be applied, accuracy of up to 2 m can be obtained for a slowly moving vehicle.