Connecting Users with Their Data: An Environment to Explore the Morphodynamics of Rip Channels

Abstract

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.

Visualizing and quantifying the movement of vegetative drought using remote-sensing data and GIS

“…The prototype Dynomap developed by Turdukulov et al (2005Turdukulov et al ( , 2007 in ENVI/IDL ® is used to extract and visualize the evolution of entities in RS images. Entities are extracted with the following attributes:…”

A Framework for Visualization and Exploration of Events

“…They represent the isolation and characterization of particular changes of interest from an otherwise overwhelming environment of changing parameters such that users can focus on the relevant change. 6 While observational data (in this context sensor-based space-time series) are an essential foundation for analysis, the abstraction of data to events can extend the analytical approach. Such a view has been noted previously.…”

Assessing vegetative drought from multi-temporal NDVI images

“…The objectbased approach is used here to analyze the movement of vegetative drought and rainfall deficit objects in space and time. To do so, I used the tracking-visualization tool, Dynomap, developed by Turdukulov et al (2005Turdukulov et al ( , 2007 in ENVI/IDL. The tool allows to extract entities with crisp boundaries and fuzzy interiors, hence accounting for the spatial variability of values within the entities.…”

“…As centroids are given in (x,y) image coordinates, a common linear transformation using image characteristics in For extracting and tracking objects, the tracking-visualization tool, Dynomap, developed by Turdukulov et al (2005Turdukulov et al ( , 2007 in ENVI/IDL ő is used.…”