Which two factors can lead to high RMS error in georeferencing?

The concept of root mean square (RMS) error in georeferencing is crucial for ensuring that spatial data aligns accurately with its real-world position. High RMS error can indicate problems in the georeferencing process, making it essential to recognize the contributing factors.

When considering the factors that contribute to high RMS error, a significant one is the presence of misplaced control points. Control points serve as reference points for transforming the coordinates of the original data to match a desired coordinate system. If these control points are incorrectly placed, even by a small margin, the entire dataset can be skewed, leading to significant deviations from their true locations. This misalignment directly contributes to an increase in RMS error, as the transformation will not accurately reflect the real-world geography.

Another contributing factor is an unsuitable transformation method. Different transformation methods can handle the alignment of data in various ways, and if the chosen method does not fit the nature of the displacement of the control points or the data itself, it can result in a poor fit and higher RMS error. Similarly, misplacement of control points and using similar control points, which can lead to inadequate coverage and representation of the spatial extent, will also impact the accuracy of the georeferencing.

Therefore, while misplaced control points directly