Coastal Depth Extraction from Satellites Images

Abstract

The problems in bathymetry measurement often have gaps or ‘holes’ within the data. As a result, hydrographic surveyors often have sparse data, and even though the data is dense and equal distances, there is still a gap in time. This paper present coastal depth extraction from satellite images. The problem encountered during the bathymetry derivation process and the problem related to the space, distribution and quantity of the Single-beam echo sounder (SBES) data. Therefore, the idea of using spatial interpolation could be a suitable approach in solving the problems. This study intends to produce Satellite-Derived Bathymetry (SDB) from Landsat 8 images at Pantai Tok Jembal, Terengganu, Malaysia. The proposed method by first interpolating the SBES point in the calibration data using spatial predictors, i.e. Inverse Distance Weightage, Thin-Plate Spline, Spline with Tension, Universal Kriging, Natural Neighbor, and Topo to Raster. Second, the raster output created from the interpolation process then converts into the point shapefile. Third, intersect function use to eliminate the point whereby not in the domain. Finally, the newly generated SBES points in calibration data ready to apply at the SDB computation process, generating SDB. In continuation, a comparative analysis conducted between six SDB results generated using each different newly generated calibration data. The result indicates SDB utilizes with Universal Kriging-newly generated calibration data (RMSE: 0.718 m) was the best result. To summarise, this study has successfully attained the research objectives by utilizing the newly generated calibration data in generating SDB. The task of spatial interpolation recreates the SBES data from irregular space and short data to uniform space and long data, which facilitate in pixel to point value extraction and help refine the bathymetry derivation process. Furthermore, the proposed method suitable to be used when the data are not applicable or limited.