Connecting users with their data: an environment to explore the morphodynamics of rip currents

Remote sensors are widely used in coastal morphodynamic research and the users are often confronted with abundant data that sensors generate. 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 currents. Coastal datasets are presently examined using animated image sequences. The users are dissatisfied because the exploration remains largely a qualitative, subjective, tedious and time-consuming process. Especially detecting the presence and evolution of relatively small, highly dynamic rip currents is difficult. In this paper, we examine how the visual exploration of rip objects can be improved. We firstly look at the factors limiting exploratory use of conventional animations for rip studies and argue that two main factors are responsible for that: data complexity and animation design based on images that mimic reality. Then we present an example of how the current approach to visualize time series of coastal images can be improved by computational methods, particularly by feature tracking. Next, we describe the visualization environment and discuss the representational, data mining and interactive functionality resulting from such a combination in an environment that is specifically dedicated to exploration of rip currents.