A new approach to satellite-derived bathymetry: the use of NDWI and ANN with bathymetry sections for reservoir mapping

Abstract:

Mapping the submerged bottom is a hampered task when traditional vessels are inserted in shallow places that pose a danger to navigation. In this sense, current research has sought optical remote sensing to obtain bathymetry estimates in larger locations in less time. However, most studies employ a large sample of bathymetric points to estimate depth with orbital images. The need for a large amount of random bathymetric points can make these procedures less viable and unattractive. Thus, the present work proposes a methodology to estimate bathymetric depths from orbital images using sections of bathymetric points previously spaced in the study area, avoiding the need for a high amount of points collected through traditional bathymetric surveys. This work also compares this methodology using the NDWI index and the ANNs. Furthermore, the study showed that the points contained in the sections are quite efficient for extracting bathymetry with orbital images, especially through the implementation of neural networks, achieving a volume estimation accuracy within 4% of the actual volume of the reservoir in question.

Keywords:
Remote Sensing; Bathymetric Sections; Artificial Intelligence; Inland Waterbodies