IceTrendr: An Automated Visualization and Analysis Tool for Mapping Changes in Glacier Extent from Landsat, North America, 1984-2015

• Main Author: Peder Nelson
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2017
• Subjects: Glacier; Climate change; LANDSAT; Wolverine Glacier; South Cascade Glacier; Gulkana Glacier; Bering Glacier; White Glacier; IceTrendr; Baffin Island; Canada; Randall; Cascade Glacier; alaska range; Baffin; glacier; glacier*; glaciers; Alaska
• Online Access: https://doi.org/10.18739/A2JD4PP9C

The major goal of this EAGER project is to create a Big Data mining toolset for the Landsat Time Series that captures, labels, and maps glacier change for use in climate science, hydrology, and Earth science education. This pilot study demonstrates the potential for interactively mapping, visualizing, and labeling glacier changes. What is truly innovative is that IceTrendr not only maps the changes but also uses expert knowledge to label the changes and such labels can be applied to other glaciers exhibiting statistically similar changes. This is much more than just a simple "then and now" approach to glacier mapping. IceTrendr is a means of integrating the power of computing, remote sensing, and expert knowledge to "tell the story€" of glacier changes. Our key findings are that the IceTrendr concept and software can provide important functionality for glaciologists and educators interested in studying glacier changes during the Landsat TM timeframe (1984-present). With additional time and funding, there is the exciting and innovative opportunity to build on the IceTrendr framework, to develop much greater utility for mapping glaciers and characterizing glacier change globally. Although this pilot study focused on just five glaciers, with some future funding and effort, IceTrendr will have the potential to map changing glaciers EVERYWHERE over the full Landsat TM timeframe (1984-present). Specifically, concerns with the Landsat TM imagery are that many images are missing during the period 1984-1995 and the automated cloud mask is not effective requiring the user to manually identify cloud-free images. We found that the visualization of the glacier in the IceTrendr window worked well with high-resolution satellite data from Google Earth and visualization was improved with additional high-resolution images from the Polar Geospatial Center. The automated clustering algorithm was a good first step in glacier mapping and when augmented with glacier outlines from the Randall Glacier Inventory, users could readily see changes in glacier extent, brightness, debris cover, as well as changes in surrounding area including glacial lakes and rivers, vegetation, and moraines.