A review and test of shoreline extraction techniques
Shoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data a...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/405648 |
| Acceso en línea: | https://hdl.handle.net/2117/405648 https://dx.doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-17-2023 |
| Access Level: | acceso abierto |
| Palabra clave: | Coasts--Remote sensing Coastal zone management Climatic changes Radar Shoreline extraction Classifier Machine learning: Multispectral Zones costaneres--Ordenació Canvis climàtics Àrees temàtiques de la UPC::Enginyeria civil::Geologia |
| Sumario: | Shoreline represents the boundary between land and sea, and its accurate extraction is of utmost importance because of the economic and ecological value of coastal areas. Nowadays, satellite remote sensing is widely used for monitoring the natural environment. Indeed, satellite remote sensing data are cost-effective and periodically available over large areas at a relatively high spatial resolution. Hence, the automatic shoreline extraction from satellite images is a fundamental task for coastal monitoring and management. Shoreline extraction methods are usually applied to satellite remote sensing data. The goal of this study is to compare the performance of different shoreline extraction methods, such as thresholding and more complex classification approaches, such as Random Forest (RF), Minimum Distance (MD), Maximum Likelihood (ML) and K-means, using both optical and radar images. The considered case study area is the shallow basin of the Orbetello Lagoon and one of its ayre called Feniglia. The data supplier is the Copernicus program, which, through the Sentinel-1 and Sentinel-2 missions, provides medium-resolution, open-access products. The accuracy of the obtained results from both methodologies is checked by validating the extracted shoreline using an aerial orthomosaic and, subsequently, a manually extracted shoreline. A preliminary accuracy assessment was performed for image classification, focusing on extracting four classes: water, soil, urban, and forest, using manual segmentation as a reference. In terms of deviation from the reference shoreline, the results obtained through the analysed methodologies achieved an accuracy of 3.75 m, less than half of the pixel size of the Sentinel-1 and Sentinel-2 used products. |
|---|