This article applies inductive reasoning for explaining how the presence of a forest cover affects the performance of diverse global navigation satellite system (GNSS) receivers. We computed GNSS accuracy and precision obtained in a Scots pine forest situated in the Guadarrama mountain range, Spain. The quality of the GNSS occupations obtained was related to forest parameters and indices describing stand density at the receiver's position. We also characterized the terrain and the canopy gap surrounding the receiver to search for more sources of variability. We computed multiple regression models by means of both ordinary and partial least squares. Results with both techniques showed that most variables were clearly determining the quality of GNSS positioning, although we had to discard the practicality of using terrain slope, stem density, or characterizing only the trees that surround the receiver. Moreover, partial least-squares analysis was successfully used to discriminate two different components that were causing opposite effects on the vertical accuracy. We regarded the second component as being caused by the separate effect of tree needles, because the higher the tree canopy was, the lower the error. Therefore, tree height may describe opposed effects: while wood stock increases, the crown bulk distances itself from the GNSS receiver. We therefore suggest that it is mandatory to model the interactive effects of both the number of trees and their size and height. Hence, we propose relative spacing index, wood volume, and leaf area index as the variables with the best potential for predicting GNSS accuracy.