Evaluating remote sensing approaches for mapping the bathymetry of Lake Manzala, Egypt

The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods.

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Evaluating remote sensing approaches for mapping the bathymetry of Lake Manzala, Egypt

Cited by 3 publications

References 30 publications

Potential of Using Machine Learning Regression Techniques to Utilize Sentinel Images for Bathymetry Mapping of Nile River

Potential of Using Machine Learning Regression Techniques to Utilize Sentinel Images for Bathymetry Mapping of Nile River

Bathymetry monitoring of shallow coastal environment using remote Sensing data

Application of Filtering Techniques to Smooth a Surface of Hybrid Digital Bathymetric Model

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