摘要：The measurement of glacier velocity fields using repeat satellite imagery has become a standard method of cryospheric research. However, the reliable discovery of important glacier velocity variations on a large scale is still problematic because time series span different time intervals and are partly populated with erroneous velocity estimates. In this study we build upon existing glacier velocity products from the GoLIVE dataset (https://nsidc.org/data/golive, last access: 26 February 2019) and compile a multi-temporal stack of velocity data over the Saint Elias Mountains and vicinity. Each layer has a time separation of 32 days, making it possible to observe details such as within-season velocity change over an area of roughly 150 000 km2. Our methodology is robust as it is based upon a fuzzy voting scheme applied in a discrete parameter space and thus is able to filter multiple outliers. The multi-temporal data stack is then smoothed to facilitate interpretation. This results in a spatiotemporal dataset in which one can identify short-term glacier dynamics on a regional scale. The goal is not to improve accuracy or precision but to enhance extraction of the timing and location of ice flow events such as glacier surges. Our implementation is fully automatic and the approach is independent of geographical area or satellite system used. We demonstrate this automatic method on a large glacier area in Alaska and Canada. Within the Saint Elias and Kluane mountain ranges, several surges and their propagation characteristics are identified and tracked through time, as well as more complicated dynamics in the Wrangell Mountains.
