## 2 Data Description
### Approximate Positional Accuracy  
The Reach RS+ rover internally calculates the position of sample points relative to a continuously updating average position [17]. The deviation from the average position is reported by the unit both on-screen and in the output files as the root mean squared error (RMSE). The RMSE is a measure of the difference between values that are predicted and those that are observed. However, because there is no 'observed' known location to which the collected sample point locations are compared, the reported values are the standard deviation. In this dataset, the RMSE field names have been changed to SD to reflect what the values represent. For this application, we report \(SD_{x}\), \(SD_{y}\), \(SD_{z}\), and the horizontal linear SD in the radial direction (\(SD_{r}\)) from Equation (1) [18]. These are referred to incorrectly as RMSE\({}_{x}\), RMSE\({}_{y}\), RMSE\({}_{z}\), and RMSE\({}_{r}\) in the original data files output by the unit. \[SD_{r}=\sqrt{SD_{x}{}^{2}+\ SD_{y}{}^{2}}, \tag{1}\]  
The SD are reported in the ground truth dataset file, as well as the attribute table of the shapefile. An approximate horizontal (\(\sim ACC_{r}\)) and vertical (\(\sim ACC_{z}\)) accuracy were calculated from Equations (2) and (3). \[\sim ACC_{r}=1.7308\ \times\ (AA_{r}+\delta_{r}), \tag{2}\]  
\[\sim ACC_{z}=1.9600\ \times\ (AA_{z}+\ \delta_{z}), \tag{3}\]  
where \(\delta_{r}\) is the uncertainty in the radial dimension (\(SD_{r}\)) and \(\delta z\) is the uncertainty in the z dimension (\(SD_{z}\)). Because the SD values represent precision, rather than accuracy compared to a known position, the manufacturer-stated approximate accuracy (AA) of 1 m for baselines < 30 km is adopted here. Equation (2) is modified from the National Standard for Spatial Data Accuracy (NSSDA) horizontal accuracy (\(ACC_{r}\)) at the 95% confidence level [18]. However, because \(ACC_{r}\) as described by [18] is calculated using the error compared to a known position, the NSSDA \(ACC_{r}\) could not be determined for this dataset, and an approximate horizontal accuracy at the 95% confidence level is reported. Equation (3) is modified from the ASPRS Accuracy Standards for Digital Geospatial Data, which notes that the vertical error in vegetated terrain does not typically follow a normal distribution [18]. Because \(ACC_{z}\) as described by [18] is calculated using the error compared to a known position, the ASPRS \(ACC_{z}\) could not be determined for this dataset and an approximate vegetated vertical accuracy at the 95 th percentile is reported. The same EMLID Reach RS+ unit with incoming corrections from SmartNet North America via NTRIP had been previously verified for accuracy using a Natural Resources Canada High Precision 3D Geodetic Network station (#95K0003) [19]. The positional accuracy was assessed to be <2.5 cm (x and y) and <3 cm (z) [20]. A brief summary of the average, minimum, maximum, and standard deviation of SD in each dimension, \(\sim ACC_{r}\), and \(\sim ACC_{z}\) for all 320 points is given in Table 4. Tables 5 and 6 contain the average, minimum, and maximum SD in each dimension, \(\sim ACC_{r}\), and \(\sim ACC_{z}\) for the ground truth points with a solution status of FIX (143 points) or FLOAT (177 points), respectively. All 320 points had an \(\sim ACC_{r}\) < 2.25 m and a \(SD_{r}\) < 16.5 cm, which meet the requirements of the project as determined by the characteristics of the HSI. All ground truth points had an \(\sim ACC_{z}\geq 1\) m, and 89.69% of all ground truth points had an \(\sim ACC_{z}\) < 2 m. Thirty-three points (10.31%) had an \(\sim ACC_{z}\geq 2\) m, but no points had an \(\sim ACC_{z}\) > 3 m. This indicates the vertical data would not be suitable for geospatial mapping projects that require high vertical accuracy, but would be suitable for visualization and projects without high vertical accuracy requirements.