Performance of Landsat 8 OLI and Sentinel 2 MSI Images Based on MNF Versus PCA Algorithms and Convolution Operators for Automatic Lithuanian Coastline Extraction

Faced with the increasing coastalization in the world, exposing populations and activities to coastal risks, decision-support tools based on the extraction of coastlines by remote sensing have become essential for measuring coastal dynamics and developing future local planning. However, these tools are constrained by factors such as the choice of data or the extraction methods, etc. which influence the reliability of the data produced. The aim of this study is to evaluate the automatic coastline extraction methods using transformation algorithms and image enhancement operators from Landsat 8 OLI and Sentinel 2 MSI satellite data. The estimates are based on the average distances and the average differences in annual variation rates between the automatically extracted coastlines and the reference coastlines digitised manually from orthophotographs. Additional validation data are used, such as the prediction rate of coastal dynamics, the digitisation rate of the study area and an overall margin of error including georeferencing errors. The results show that coastlines modelled using the Principal Component Analysis transformation method with High Pass and Laplacian image enhancement filters generate the best performances. The good accuracy of coastline recognition with Sentinel 2 MSI satellite images coastline could be related to spatial and spectral resolution factors. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.