How can remote sensing data/techniques help us to understand beach hydromorphological behavior?

Be ach morphological classification was mainly established for Australian and American microtidal sandy coasts. Different beach morphologies and classification models were presented by several authors. However, parameters such as Hb (wave breaking height) and tan� (beach slope) are usually unavailable or simply nonexistent for many coastal areas. Therefore, without this information, the morphologic analysis of remotely sensed data from several years is a good approach to identify and to classify beach morphologies. Remote sensing data is an increasingly important component of natural resource monitoring programs and data collection. The aim of this study is to apply different image processing algorithms to different remotely sensed data (aerial photographs and IKONOS image) in order to identify coastal features/patterns. To achieve that, pixel-based, object-based classification algorithms, a hybrid method (called Principal Components Analysis and Histogram), and a pattern recognition approach (using Neural Networks) were applied to remotely sensed data (aerial photographs and IKONOS-2 image). Regarding the results of this work, showing that pixel-based classification (supervised classification algorithms with overall accuracy>99% and Kappa Statistic>0.98) archived better results in comparison with object-based classification; the addition of a NiR band is useful in the classification procedure; and remote sensing data is very useful to identify coastal forms/patterns, helping on the classification of beach morphology, especially in coastal areas where data records are scarce or inexistent.