Visualizations of coastal terrain time series

In coastal regions, water, wind, gravitation, vegetation, and human activity continuously alter landscape surfaces. Visualizations are important for understanding coastal landscape evolution and its driving processes. Visualizing change in highly dynamic coastal terrain poses a formidable challenge; the combination of natural and anthropogenic forces leads to cycles of retreat and recovery and complex morphology of landforms. In recent years, repeated high-resolution laser terrain scans have generated a time series of point cloud data that represent landscapes at snapshots in time, including the impacts of major storms. In this article, we build on existing approaches for visualizing spatial–temporal data to create a collection of perceptual visualizations to support coastal terrain evolution analysis. We extract terrain features and track their migration; we derive temporal summary maps and heat graphs that quantify the pattern of elevation change and sediment redistribution and use the space–time cube concept to create visualizations of terrain evolution. The space–time cube approach allows us to represent shoreline evolution as an isosurface extracted from a voxel model created by stacking time series of digital elevation models. We illustrate our approach on a series of Light Detection and Ranging surveys of sandy North Carolina barrier islands. Our results reveal terrain changes of shoreline and dune ridge migration, dune breaches and overwash, the formation of new dune ridges, and the construction and destruction of homes, changes which are due to erosion and accretion, hurricanes, and human activities. These events are all visualized within their geographic and temporal contexts.

[1]  Ho Van Quan,et al.  Exploratory 3D geovisual analytics , 2008, 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies.

[2]  Susanne Bleisch,et al.  Rich point clouds in virtual globes - A new paradigm in city modeling? , 2010, Comput. Environ. Urban Syst..

[3]  Christopher G. Healey,et al.  Choosing effective colours for data visualization , 1996, Proceedings of Seventh Annual IEEE Visualization '96.

[4]  Helena Mitasova,et al.  Geospatial analysis of a coastal sand dune field evolution: Jockey's Ridge, North Carolina , 2005 .

[5]  Jun-Yong Park,et al.  Spit Growth and Downdrift Erosion: Results of Longshore Transport Modeling and Morphologic Analysis at the Cape Lookout Cuspate Foreland , 2007 .

[6]  Per Ola Kristensson,et al.  An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns , 2009, IEEE Transactions on Visualization and Computer Graphics.

[7]  Xia Li NEW VIEWS ON MULTIVARIABLE SPATIO-TEMPORAL DATA : THE SPACE TIME CUBE EXPANDED , 2005 .

[8]  Heidrun Schumann,et al.  Space, time and visual analytics , 2010, Int. J. Geogr. Inf. Sci..

[9]  Donald G. Janelle,et al.  Geovisualization of Human Activity Patterns Using 3 D GIS : A Time-Geographic Approach , 2002 .

[10]  Cynthia A. Brewer,et al.  ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps , 2003 .

[11]  Helena Mitasova,et al.  Landscape dynamics from LiDAR data time series , 2011 .

[12]  H. Mitásová,et al.  Raster-Based Analysis of Coastal Terrain Dynamics from Multitemporal Lidar Data , 2009 .

[13]  Alex Pang,et al.  Visualizing Uncertainty in Geo-spatial Data , 2001 .

[14]  Guoqing Zhou,et al.  Coastal 3-D Morphological Change Analysis Using LiDAR Series Data: A Case Study of Assateague Island National Seashore , 2009 .

[15]  Yong Wang,et al.  Utilizing DEMs derived from LIDAR data to analyze morphologic change in the North Carolina coastline , 2003 .

[16]  Sarah F. Tebbens,et al.  Dune Retreat and Shoreline Change on the Outer Banks of North Carolina , 2008 .

[17]  Daniel A. Keim,et al.  Challenging problems of geospatial visual analytics , 2011, J. Vis. Lang. Comput..

[18]  Gennady L. Andrienko,et al.  Exploratory spatio-temporal visualization: an analytical review , 2003, J. Vis. Lang. Comput..

[19]  Gennady L. Andrienko,et al.  Interactive analysis of event data using space-time cube , 2004, Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004..

[20]  Menno-Jan Kraak,et al.  Visualization of spatio - temporal patterns of public transport data , 2005 .

[21]  Pamela D. Tucker,et al.  National Assessment Of Shoreline Change: Part 2, Historical Shoreline Changes And Associated Coastal Land Loss Along The U.S. Southeast Atlantic Coast , 2005 .

[22]  H. Mitásová,et al.  Least Cost Path Extraction of Topographic Features for Storm Impact Scale Mapping , 2012 .

[23]  Menno-Jan Kraak,et al.  Developing a Geovisual Analytics Environment for Investigating Archaeological Events: Extending the Space–Time Cube , 2009 .

[24]  Ian L. Turner,et al.  CZM Applications of Argus Coastal Imaging at the Gold Coast, Australia , 2004 .

[25]  Helena Mitasova,et al.  SUMMARY VISUALIZATIONS FOR COASTAL SPATIAL-TEMPORAL DYNAMICS , 2013 .

[26]  Helena Mitasova,et al.  New spatial measures of terrain dynamics derived from time series of lidar data , 2009, 2009 17th International Conference on Geoinformatics.

[27]  Menno-Jan Kraak,et al.  The space - time cube revisited from a geovisualization perspective , 2003 .

[28]  Menno-Jan Kraak,et al.  New views on multivariable spatio - temporal data : the space time cube expanded , 2005 .

[29]  Hartmut Asche,et al.  Geovisualization Approaches for Spatio-temporal Crime Scene Analysis - Towards 4D Crime Mapping , 2009, IWCF.

[30]  Torsten Hägerstraand WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .