Claudio Parente

The coastal environment represents a resource from both a natural and economic point of view, but it is subject to continuous transformations due to climate change, human activities, and natural risks. Remote sensing techniques have enormous potential in monitoring coastal areas. However, one of the main tasks is accurately identifying the boundary between waterbodies such as oceans, seas, lakes or rivers, and the land surface. The aim of this research is to evaluate the accuracy of coastline extraction using different datasets. The images used come from UAV-RGB and the Landsat-9 and Sentinel-2 satellites. The method applied for extracting the coast feature involves a first phase of application of the Normalized Difference Water Index (NDWI), only for satellite data, and consequent application of the maximum likelihood classification, with automatic vectorization. To carry out a direct comparison with the extracted data, a coastline obtained through a field survey using a Global Navigation Satellite System (GNSS) device was used. The results are very satisfactory as they meet the minimum requirements specified by the International Hydrographic Organization (IHO) S-44. Both the UAV and the Sentinel-2 reach the maximum order, called the Exclusive order (Total Horizontal Uncertainty (THU) of 5 m with a confidence level of 95%), while the Landsat-9 falls into the Special order (THU of 10 m with a confidence level of 95%).