*2.2. Data*

We used the high-density LiDAR data, under the license of the National Land Survey of Finland (NLS), recorded in 2020 for the selected stands. The data have a density of greater than 5 points/m<sup>2</sup> along with horizontal and altimetric errors of less than 45 and 10 cm [40], respectively. The features affecting the positioning accuracy of the u-blox ZED-F9P were mapped from the LiDAR-derived metrics such as the digital terrain model (DTM), digital surface model (DSM), point density, and signal intensity. Logging trails were detected from the high-density LiDAR data based on the U-Net convolutional neural network approach developed by Abdi et al. [25]. In addition, we obtained orthophotos from the databases of NLS [41]. The attributes of the forest stands were collected from the databases of Finsilva Oy.

#### *2.3. GNSS Devices*

Three GNSS receivers were used, including an Oregon® 750t (Garmin Ltd., Olathe, KS, USA), a u-blox ZED-F9P (u-blox, Thalwil, Switzerland), and a Trimble R2 (Trimble Inc., Sunnyvale, CA, USA), for specialised applications during our field operations.

The Oregon® 750t receiver was used for navigating the approximate locations of the selected logging trails.

We used the u-blox ZED-F9P to identify features affecting the positioning accuracy of the low-cost GNSS receivers in the forest. The u-blox ZED-F9P is a multi-band GNSS receiver that can measure positions at centimetre-level accuracies in RTK mode. This receiver obtains signals from multiple bands (L1, L2/E5b/B21) of all four global GNSS constellations including GPS, Galileo, BeiDou, and GLONASS via a Multi band GNSS antenna ANN-MB-00 (SMA) (u-blox, Thalwil, Switzerland) [42]. The antenna is designed in a small, compact size. It can be easily mounted on different machinery due to its magnetic fixed installation base and a long cable of5m[43]. The GNSS and RTK integration has accelerated its convergence time (down to less than 10 sec) [42]. Moreover, performance with the application for conducting unmanned autonomous vehicles (UAV), automatic and semi-automatic machinery, and robotic machines has improved [44]. The u-blox F9P was equipped with advanced anti-spoofing and anti-jamming algorithms that guarantee highly accurate positioning and navigation information. The receiver and antenna are both waterproof and can also operate under extreme temperatures (−40 ◦C to +85 ◦C) [45].

We used the Trimble R2 receiver, paired with Trimble TSC7 (Trimble Inc., Sunnyvale, CA, USA), for collecting accurate control points during recording data through u-blox F9P. The Trimble R2 can acquire high positioning accuracy in RTK mode both horizontally (1 cm to 1 ppm RMS) and vertically (2 cm to 1 ppm RMS).

#### *2.4. Research Sulky*

We modified a sulky for transporting the u-blox and its compartment for recording positioning data in the forest. The sulky includes two bicycle wheels of 28 in width. The length, width, and height of the main body of the sulky are 1.2 m, 65 cm, and 87 cm, respectively. It was equipped with a veneer plate (103 cm × 53 cm) for holding the devices. A thin plate (50 cm × 40 cm) was installed on the veneer plate for fixing the magnetic antenna. The sulky was controlled by a draught pole (adjustable up to 1.3 m), which includes a trapezoid-shaped handle to make pulling easier for the user (Figure 2).

#### *2.5. Field Measurements*

We established the local base stations to send corrected data to the rover receiver in the vicinity of our study site (Figure 1a). The base and movable stations were developed using a SparkFun GPS-RTK2 Board (SparkFun Electronics, Boulder, CO, USA) with ublox-ZED-F9P module and the SMA. The receivers of the base stations were configured based on an NTRIP-protocol via the internet server (rtk2go.com) to provide RTCM V3.2 standard correction signals.

We selected a number of logging trails with about 2 km for our experiment (Figure 1c). The shapefiles of the logging trails were converted into GPS Exchange Format (GPX) and imported into the Garmin device for spotting out the logging trails in the field.

We started our measurement from 8:32 a.m. and ended at 12:37 p.m. (GMT). The routes were so designed to pass through different species with diversity in age, height, density, canopy cover, and topographic conditions. Our speed was close to the normal speed of harvesters and forwarders (i.e., 44 to 56 m/min) in the forest.

The u-blox ZED-F9P receiver and its compartments, including the SAM, 4G TP-LINK M7200 modem (TP-Link Technologies Co., Shenzhen, China), and Laptop computer, were mounted on a research sulky that was designed for this purpose (Figure 2). The configuration period of the receiver was set to 1 Hz. All four GNSS constellations were selected to receive adequate and strong signals to acquire positions by the u-blox F9P in RTK mode. A Raspberry Pi minicomputer with RTK-LIB open-source program was used for

operating the RTK-station. We used u-center evaluation software program to monitor and process all aspects of recorded data (e.g., horizontal and vertical positions, accuracies, time, tracking of GNSS constellations, etc.) during the operation of the u-blox GNSS receiver [46]. All recorded data were captured in ASCII format, imported into ArcGIS (Esri, Redlands, CA, USA), and integrated with the object features to be used for TreeNet analysis.

**Figure 2.** Compartments for field experiment: (**a**) research sulky, (**b**) u-blox ZED-F9P, (**c**) Multi band GNSS antenna ANN-MB-00 (SMA), (**d**) modem, and (**e**) computer system.

We recorded the positions of 64 control points on the logging trails with an accuracy of less than 1 cm by the Trimble R2, as references, in RTK mode. The absolute errors were calculated between the measured positions by the u-blox F9P and the positions of the control points. The one-sample t-test was used to determine whether the mean of errors exceeded the optimal directions of an image pixel (i.e., 50 cm) derived from the high-density LiDAR data at a significance level of 0.05.
