Skip to content

1 paper indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Review Open access Aug 2026

Comparison of Shoreline Determination Methods Using Multi-Sensor Data in Low-Relief Coastal Environments

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable.

Ivar Kapsi, Tarmo Kall, K. Türk et al. · 0 citations