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In the past decade, the availability of, and ability to process, remote sensing data over glaciers has expanded tremendously. Newly opened satellite image archives, combined with new processing techniques as well as increased computing power and storage capacity, have given the glaciological community the ability to observe and investigate glaciological processes and changes on a truly global scale. In particular, the opening of the ASTER archives provides further opportunities to both estimate and monitor glacier elevation and volume changes globally, including potentially on sub-annual timescales. With this explosion of data availability, however, comes the challenge of seeing the forest instead of the trees. The high volume of data available means that automated detection and proper handling of errors and biases in the data becomes critical, in order to properly study the processes that we wish to see. This includes holes and blunders in digital elevation models (DEMs) derived from optical data or penetration of radar signals leading to biases in DEMs derived from radar data, among other sources. Here, we highlight new advances in the ability to sift through high-volume datasets, and apply these techniques to estimate recent glacier volume changes in the Caucasus Mountains, Scandinavia, Africa, and South America. By properly estimating and correcting for these biases, we additionally provide a detailed accounting of the uncertainties in these estimates of volume changes, leading to more reliable results that have applicability beyond the glaciological community.
|Publication status||Published - 14 Dec 2017|
|Event||AGU Fall Meeting - New Orleans, United States|
Duration: 11 Dec 2017 → 15 Dec 2017
|Conference||AGU Fall Meeting|
|Period||11/12/17 → 15/12/17|
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- 1 Invited talk