Beach hydromorphological analysis through remote sensing

Beach hydromorphological classification is a complex subject. Different beach classification models were presented by several authors. However, fundamental parameters are usually unavailable. Therefore, a morphological analysis using remotely sensed data and image processing techniques is a good approach to identify and to classify beach hydromorphologies. Remote sensing data is an increasingly important component of natural resources monitoring programs. Its usefulness can be maximized by understanding the constraints and capabilities of the imagery and change detection techniques, related to the monitoring objectives. The aim of this study was to explore different remotely sensed data (aerial photographs and a satellite image) and different image processing algorithms in order to identify coastal forms/patterns and further classify beach hydromorphological stage. To achieve that, different image processing techniques were applied to remotely sensed data: pixel and object-based classification algorithms and a pattern recognition approach using artificial neural networks. A stretch of the northwest coast of Portugal was chosen as the study area. The data used in this study consisted in aerial photographs and an IKONOS-2 image. Based on the obtained results two main conclusions could be taken: the pixel-based classification (supervised classification algorithms) showed better results than the object-based classification algorithms; and the pattern recognition approach is the most effective and accurate methodology. Therefore, the association of remote sensing data and image processing techniques is very useful in identifying coastal forms/patterns regarding the classification of beach morphological stage.