Coastlines worldwide are coming under increasing pressure due to climate change and human activity. Data on shoreline change are essential for coastal managers and when no long-term monitoring programs are implemented and shoreline change is typically on the order of less than 1 m/yr., as observed in Ireland, aerial photography is the most valuable source of information. A well-established literature exists for automated vegetation extraction from digital images based on the near infrared reflectance, but there is less research available on spectrally limited colour photography. This study develops a methodology for automating vegetation line extraction from a series of historical aerial photography of the Cork coastline in the South-West of Ireland. The approach relies on the Normalised Green–Blue Difference Index (NGBDI), which is versatile enough to discriminate disparate coastal vegetation environments, at different resolutions and in various lighting and seasonal conditions. An iterative optimal threshold process and the use of LiDAR ancillary datasets resulted in an automated vegetation line measurement with uncertainties estimated to be between 0.6 and 1.2 m. Change rates derived from the vegetation lines extracted present uncertainties in the range of ±0.27 m/yr. This robust and repeatable method provides a valuable alternative to time-consuming and subjective manual digitisation.