Beach Hydromorphological Analysis Through Remote Sensing

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Ana Teodoro (Autor) FCUP; Joaquim Pais Barbosa (Autor) Outra; Hernani Goncalves (Autor) FCUP; Fernando Veloso Gomes (Autor) FEUP; Francisco Taveira Pinto (Autor) FEUP

Revista

Título: Journal of Coastal Research

Vol. 61

Páginas: 44-51

ISSN: 0749-0208

Editora: Coastal Education & Research Foundation, Inc.

Indexação

ISI Web of Knowledge - 1 Citação

ISI Web of Science

Scopus - 2 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da terra e ciências do ambiente

Outras Informações

ID Authenticus: P-002-W44

DOI: 10.2112/si61-001.55

Abstract (EN): TEODORO, A., PAIS-BARBOSA, J., GONCALVES, H., VELOSO-GOMES, F and TAVEIRA-PINTO, F., 2011. Beach Hydromorphological Analysis Through Remote Sensing. In: Micallef, A. (ed.), MCRR3-2010 Conference Proceedings, Journal of Coastal Research, Special Issue, No. 61, pp. 44-51. Grosseto, Tuscany, Italy, ISSN 0749-0208. 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8