DETECTING BATHYMETRY FROM LANDSAT 8 - OLI IMAGERY FOR VARIABLE SITES OF SUSPENDED SEDIMENT AND BOTTOM FEATURES

Abstract

Egypt has coastlines along the Mediterranean Sea, Red Sea, and Suez Canal, which are attractive environments for tourism, significant income sources and have a large population. Coastal zones are demanding highly continuous assessment and monitoring. Bathymetry is an essential requirement for that assessment and management. The traditional technique for bathymetry data in Egypt is Single Beam Echosounder, which has limitations as limited covered area, excessive cost, severe weather conditions, and time-consuming. Landsat satellites have brought a new advance of a quick, and low-cost tool for collecting bathymetric information. The scope of the current study is to investigate the performance of Landsat 8 OLI derived bathymetry in detecting bathymetry using the following empirical methods; linear band, ratio transforms, and artificial neural networks. The satellite images must be atmospherically corrected to eliminate light scattering and atmospheric errors. Therefore, the second scope of the study is to investigate three different atmospheric correction methods known as FLAASH (Fast Line of Sight Atmospheric Analysis of spectral Hypercubes), DOS (Dark Object Subtraction), and QUAC (Quick Atmospheric correction). The models have been applied over six sites that have different seabed characteristics and water quality, four along the Mediterranean Sea (North Coast, Kutishner, Jaffera, and Damietta), and two along the Red sea (Sahl Hashesh and Sokhna at Suez Gulf). The results showed that the best empirical method is artificial neural networks with different sites, and DOS atmospheric correction then QUAC for different sites especially for turbid and low water quality sites.