Time Series Of Elevation Models Research Articles

Mapping Retrogressive Thaw Slumps Using Single-Pass TanDEM-X Observations

Vast areas of the Arctic host ice-rich permafrost, which is becoming increasingly vulnerable to terrain-altering thermokarst in a warming climate. Among the most rapid and dramatic changes are retrogressive thaw slumps. These slumps evolve by a retreat of the slump headwall during the summer months, making them detectable by comparing digital elevation models over time using the volumetric change as an indicator. Here, we present and assess a method to detect and monitor thaw slumps using time series of elevation models applied on two contrasting study areas in Northern Canada. Our two-step method is tailored to single-pass InSAR observations from the TanDEM-X satellite pair, which have been acquired since 2011. For each acquisition, we derive a digital elevation model and uncertainty estimates. In the first step, we difference digital elevation models and detect the significant elevation changes using a blob-detection algorithm. In the second step, we classify the detections into those due to thaw slumps and other causes using a simple thresholding method (accuracy: 78%), a random forest classifier (87%), and a support vector machine (86%). When our method is applied to other areas, the classifiers should be trained with data from part of the study area or with data obtained from similar areas in terms of topography, vegetation, and thaw slump characteristics to achieve the best performance. The obtained locations of thaw slumps can be used as a starting point to extract important slump properties, such as the headwall height and the volumetric change, which are currently not available on regional scales.

SUMMARY VISUALIZATIONS FOR COASTAL SPATIAL-TEMPORAL DYNAMICS

Digital scans of dynamic terrains such as coastal regions are now being gathered at high spatial and temporal resolution. Although standard tools based on geographic information systems (GIS) are indispensable for analyzing geospatial data, they have limited support to display time-dependent changes in data and information such as statistical distributions and uncertainty in data. We present a set of techniques for visually summarizing the dynamics of coastal dunes. We visualize summary statistics of important data attributes and risk or vulnerability indices as functions of both spatial and temporal dimensions in our data and represent uncertainty in the data set. We apply standard techniques, the space time cube and clustering, in novel ways to the domain of geomorphology. We combine surface-mapping and imagery with summary visualizations to retain important geographical context in the visualizations and reduce clutter due to direct plotting of statistical data in displays of geospatial information. We also address some issues pertaining to visualization of summary statistics for geographical regions at varying scales. We demonstrate visualization tools on time series of elevation models from the Outer Banks of North Carolina and observe temporal-spatial trends therein.