**4. Error estimation**

The two main independent sources of uncertainty mentioned in your statement are the uncertainty caused by the georeferencing process and the uncertainty caused by the digitizing process. These two independent sources of uncertainty, namely georeferencing and digitizing, contribute to the overall uncertainty in the spatial data analysis and should be considered when interpreting or using the data. The georeferencing process involves aligning spatial data to a known coordinate system or reference imagery. The error associated with this process is assumed to be normally distributed with a mean of 0 and a standard deviation equal to the Root Mean Square Error (RMSE) resulting from the georeferencing procedure. In this case, the RMSE is ±0.5 pixels. The assumption of normal distribution implies that the errors are symmetrically distributed around the mean, and the RMSE provides an estimate of the typical magnitude of the georeferencing errors. Digitizing refers to the process of converting analogue or physical data into digital form. In this context, it involves delineating the boundary of an area on a map or image. The error associated with this process is assumed to be uniformly distributed, ranging between 0 square meters and 900 square meters. This assumption implies that the digitizing errors have an equal likelihood of occurring within this range. A mixed pixel occurs when a pixel represents a mixture of different land cover types, in this case, soil and water along the coastline. This can be a result of the spatial resolution of the data, where a single pixel covers an area that includes both land and water. The presence of mixed pixels along the coastline can introduce additional uncertainty in the analysis, as the classification or interpretation of such pixels becomes more challenging (**Figure 3**).

**Figure 3.** *Changes in coastline from 1975 to 2022.*
