## 3 Results
### Assessment of the Positioning Accuracy of the Trimble R12 GNSS Receiver Mounted on a Harvester

Table 2: Assessment of the geometric quality of GNSS signals for determining position solutions with a Trimble R12 GNSS receiver showing significant difference from pairwise test \(p<0.001\). Figure 5: Canopy cover around the measured positions. significant difference in the canopy cover estimated from the canopy height model for 'Accurate' positions (M = 39.51%) and 'Inaccurate' positions (M = 62.75%); t(1217) = \(-\)34.46, \(p\) = 0.000). A simple linear regression was performed to predict the PDOP based on the canopy cover above the roof level of the harvester, as estimated from the CHM. The regression equation was found to be significant (F(1,5563) = 15,499,106, \(p\) < 0.000), with an R\({}^{2}\) of 0.735. The equation: PDOP = 0 + 4.309 \(*\) canopy cover, %. Another regression was calculated to predict the maximum possible deviation from the total station in northing or easting directions, as estimated by the Trimble R12 based on the canopy cover above the roof of the harvester, calculated based on the CHM. The regression equation was found to be significant (F(1,5563) = 5,512,439, \(p\) < 0.000), with an R\({}^{2}\) of 0.497. The equation: maximum deviation = 0 + 0.733 \(*\) canopy cover, %. This means that the maximum deviation from the true position increased by 0.733 cm for each percentage increase in the canopy cover. The Impact of Canopy Cover on the Positional Accuracy of the Trimble R12 as Enulated by Conducting a Viewshed Analysis of the Digital Surface Model  
In order to take into account the specific factors influencing how visible the satellites are from a GNSS device vantage--differences in elevation, variations in tree height, species variations, crown size variations, and distances to many trees of different forms and sizes--a viewshed analysis of the DSM was carried out for each point (Figure 6, Table 4). The individual height of each point was used to calculate the viewshed within the DSM. Due to the complexity of the calculations (129 h for 1879 positions on 12 th Gen Intel(R) Core(TM) i9-12900 HK 2.90 GHz with NVIDIA GeForce RTX 3080 Ti Laptop GPU), a viewshed analysis was carried out for 573 randomly selected 'Inaccurate' and 1306 randomly selected 'Accurate' points. A paired-samples \(t\)-test was conducted to compare differences in the canopy cover estimated by the viewshed analysis of the DSM for 'Accurate' and 'Inaccurate' positions. There was a significant difference in the canopy cover estimated from the canopy height model for 'Accurate' positions (M = 51.50%) and 'Inaccurate' positions (M = 61.69%); t(903) = -37.18, \(p\) = 0.000). A simple linear regression was performed to predict the PDOP based on the canopy cover estimated by the viewshed analysis of the DSM. The regression equation was found to be significant (F(1,1879) = 5,508,671, \(p\) < 0.000), with an R\({}^{2}\) of 0.745. The equation was PDOP = 0 + 4.132 \(*\) canopy cover, % from the viewshed analysis of the DSM. Another regression was calculated to predict the maximum possible deviation from the total station in northing or easting directions, as estimated by the Trimble R12 based on the canopy cover estimated by the viewshed analysis of the DSM. The regression equation was found to be significant (F(1,1879) = 2,424,240, \(p\) < 0.000), with an R\({}^{2}\) of 0.563. The equation was maximum deviation = 0 + 0.774 \(*\) canopy cover %, from the viewshed analysis of the DSM. This means that the maximum deviation from the true position increased by 0.774 cm for each percentage increase in the canopy cover estimated from the viewshed analysis. To predict the number of satellites that were visible to the Trimble R12 based on the canopy cover estimated in the viewshed analysis of the DSM, we performed another regression. The regression equation was found to be significant (F(1,1879) = 7,070,107, \(p\) < 0.000), with an R\({}^{2}\) of 0.790.