Beschreibung:
<jats:p> Remote sensors are widely used in coastal morphodynamic research, and users are often confronted with abundant data sensors generated by these sensors. These data, if properly explored, can be used to study and manage many coastal features and processes that are not well understood. As an example, we present the exploration of rip channels. Coastal data sets are currently explored using animated image sequences. Users are dissatisfied, however, because exploration remains largely a subjective and time-consuming process. In particular, detecting the presence and evolution of relatively small, highly dynamic rip channels has proved difficult. This article examines how the visual exploration of rip objects can be improved. We first look at the factors limiting exploratory use of conventional animations for rip studies and argue that two main factors are responsible: data complexity and animation design based on images that mimic reality. Then we present an example of how the current approach to visualizing time series of coastal images can be improved by computational methods, particularly by feature tracking. Next, we describe a visualization prototype and discuss the representational, data-mining, and interactive functionality resulting from such a combination in an environment dedicated to the exploration of dynamic objects. </jats:p>