Shoreline mapping: how do fuzzy sigmoidal, bayesian, and demspter-shafer classifications perform for different types of coasts?

Abstract

This study examined Fuzzy Sigmoidal, Bayesian, and Demspter-Shafer classifications to map shorelines in Kuala Terengganu, Malaysia for different types of coasts. These three soft classification methods were applied to simulated Satellite Pour l’Observation de la Terre-5 (SPOT-5 with 10 and 20 m spatial resolutions) images covering the three shoreline locations which were typically different in shape and orientation: (a) rocky breakwater coasts (CT-1), (b) rocky and sandy airport coasts (CT-2), and (c) sandy beach coasts (CT-3) for predicting shorelines. Visual inspection and statistical measures showed that variations in accuracies were evident, predominantly due to differences in CTs; mapping accuracy decreased with an increase of spatial resolution, but accuracy increased if shorelines were aligned exact parallel to the column of pixel grid. Sigmoidal can predict shoreline over the CT-3 areas with greater accuracy than the other two methods and has less dependence on spatial resolutions for across the CTs. But effective use of Bayesian membership function for mapping shoreline over certain small and narrow jetty-like structures makes this classifier also efficient. The soft classifier assessed in this study produced reliable shoreline maps and should help shoreline change detection and monitoring from coarser spatial resolution imagery.