A new method for evaluating satellite-derived waterline detection in macrotidal beaches with complex intertidal morphology

Satellite-Derived Waterlines (SDWs) have become highly valuable assets in coastal studies due to their extensive data availability, offering temporal and spatial resolutions of up to 5 days and 10 m, respectively. Numerous tools for SDW extraction have been developed, being widely used in microtidal beaches with high reliability. However, macrotidal environments present significant challenges due to their large intertidal extensions. The dynamic nature and complex morphology of these areas frequently lead to inaccuracies in shoreline detection by existing tools. Furthermore, the large volume of SDWs makes identifying errors challenging. Understanding misdetection conditions is key to automating error flagging and improving efficiency.

This study aims at improving the understanding of waterline (mis)detection by identifying the environmental conditions that influence incorrect identification of the sand-water interface. SDWs extracted using the SHOREX tool, developed by the Geo-Environmental Cartography and Remote Sensing Group from the Universitat Politècnica de València, were analyzed in Salinas (144 SDWs) and El Puntal (141 SDWs), two macrotidal beaches in northern Spain with complex intertidal topography.

The analysis was undertaken with the aim of taking a step back to understand what beach features are identified as waterline by currently available tools. This involved a detailed visual inspection of SDWs compared to their corresponding RGB imagery, conducted by a validated operator. The beaches were discretized into equally spaced transects, and for each SDW, the operator classified the detected feature in each transect as one of the following: Waterline (sand-water interface), Maximum High Tide Level (dry-wet sand interface), Intertidal Water (boundary of accumulated water in the intertidal zone), Intertidal Morphological Features (dry-wet sand interface due to intertidal bars), Backshore elements, or Clouds. Three analyses were derived: (1) the percentage of transects classified as each indicator per SDW, (2) the confidence level perceived by the operator for each indicator, and (3) the correlation between met-oceanic variables (e.g., wave height, peak period, storm surge, astronomical tide, and tidal stage) and the percentage of Waterline identification per SDW.

The results revealed a strong positive correlation (R=0.56) between the percentage of transects classified as waterline (ideal identification) and a variable combining tidal level and phase (flood/ebb). Better detections during high tides likely occurred due to drier intertidal sand, while wet sand during ebb tides led to detection problems. However, a lack of representation of the highest tidal states was observed in the satellite time series. Wave parameters (Hs and Tp​) showed weaker inverse correlations to the percentage of waterline detection (R=−0.15 and −0.28, respectively), likely due to increased sand saturation during the rundown phase of energetic waves. High vertex count correlated positively with waterline identification (R=0.51), indicating improved detection with greater shoreline detail, while strong negative correlation with SDW sinuosity (R=−0.51) suggested misdetection due to complex intertidal features.

This new approach advances understanding of SDW detection in macrotidal beaches, paving the way for improving detection methodologies. Ongoing work includes assessing additional SDW detection tools, extending analyses to diverse beach types (depending on hydro-morphodynamic conditions), and developing methods for automatic error flagging in each environment.