SAET: a new tool for automatic shoreline extraction with subpixel accuracy for characterising shoreline changes linked to coastal storms

Autor: Jesús Palomar-Vázquez, Jaime Almonacid-Caballer, Carlos Cabezas-Rabadán, Josep E. Pardo-Pascual
Rok vydání: 2022
Zdroj: EGU General Assembly 2022
DOI: 10.5194/egusphere-egu22-9857
Popis: SAET (Shoreline Analysis and Extraction Tool) is a tool intended to enable the automatic detection and quantification of the changes experienced by the shoreline position on beaches affected by coastal storms. It is an open-source tool developed within the framework of the ECFAS project which aims to demonstrate the technical and operational feasibility of a European Coastal Flood Awareness System. SAET takes advantage of the freely-available images from the Sentinel satellites of ESA's Copernicus program and the Copernicus Contributing Missions. The tool currently uses the mid-resolution images of the Sentinel 2 and Landsat 8 satellites, although in the future it will allow the use of images from other satellites (as the recently available Landsat 9).In order to characterize the shoreline changes caused by a coastal storm at a certain coastal segment, SAET identifies, downloads, and processes the most suitable satellite images (those closest in time and with low cloud coverage). The shoreline extraction starts by an approximate definition of the shoreline position at pixel level using the AWEINSH water index. Subsequently, the subpixel extraction algorithm is applied over dynamic coastal stretches not affected by clouds operating over the Short-Wave Infrared bands. For each of the analysed images, the process results in the obtention of satellite-derived shorelines in vector format. Analysis of shoreline position changes is intended to offer quantitative data about the state of beaches in terms of erosion/accretion,and about their response subsequent capacity to recover after storm episodes. The ECFAS (European Coastal Flood Awareness System) project (https://www.ecfas.eu/) has received funding from the EU H2020 research and innovation programme under Grant Agreement No 101004211.
Databáze: OpenAIRE