*4.2. The Performance of TreeNet*

This study applied high-density LiDAR data and a novel object-based TreeNet approach to determine influential features that degrade the positioning accuracy of the novel developed low-cost GNSS receivers in a forest context. Earlier studies mostly modelled the influential variables of positioning accuracy using traditional regression models [12,14,17,66], which are limited with few variables and data. Conversely, we adopted TreeNet, as one of the most powerful machine learning algorithms, with remarkable abilities in handling big data and numerous variables without any preselection, pre-processing, or reduction in dataset. It reveals that a combination of forest characteristics and terrain features express the positioning errors of GNSS receivers. However, the importance of features are different.
