Inferring coastal slope of sandy beaches from remote sensing imagery and tidal level data

Abstract

Sandy beaches, comprising approximately 31% of global coastlines, play a significant role in providing both socio-economic and ecological-environmental benefits. Coastal slope is a crucial parameter in characterizing the morphology of sandy beaches, but traditional topography and bathymetry measurement methods are often limited by high costs and tidal level variations. As a result, it is challenging to obtain coastal slope information. In this study, we introduced a novel method to derive coastal slope information by utilizing multi-temporal remote sensing imagery. Specifically, a linear regression model was constructed by integrating the shoreline locations extracted from multi-temporal remote sensing images and corresponding tidal levels, and the coastal slope could be estimated by the linear regression. We applied this method to obtain coastal slopes for sandy beaches along the Sydney coastline, compared the results with the in-situ measurement as the validation, and achieved a good accuracy (RMSE = 0.01, RME = 10%, r = 0.78). The results demonstrate the reliability of the proposed method for coastal slope estimation and highlight its potential for beach slope estimation on a global scale.