Velocity variations at Columbia Glacier captured by particle filtering of oblique time-lapse images
This provides a robust method for glacier velocity monitoring using oblique cameras, but it is incremental as it adapts existing particle filtering techniques to this domain.
The researchers developed a probabilistic particle filtering method to track glacier surface motion from time-lapse imagery, which they applied to Columbia Glacier and found it transitions between winter moderate velocities, early summer speed-ups sensitive to meltwater, and fall slowdowns consistent with seasonal subglacial hydrologic network changes.
We develop a probabilistic method for tracking glacier surface motion based on time-lapse imagery, which works by sequentially resampling a stochastic state-space model according to a likelihood determined through correlation between reference and test images. The method is robust due to its natural handling of periodic occlusion and its capacity to follow multiple hypothesis displacements between images, and can improve estimates of velocity magnitude and direction through the inclusion of observations from an arbitrary number of cameras. We apply the method to an annual record of images from two cameras near the terminus of Columbia Glacier. While the method produces velocities at daily resolution, we verify our results by comparing eleven-day means to TerraSar-X. We find that Columbia Glacier transitions between a winter state characterized by moderate velocities and little temporal variability, to an early summer speed-up in which velocities are sensitive to increases in melt- and rainwater, to a fall slowdown, where velocities drop to below their winter mean and become insensitive to external forcing, a pattern consistent with the development and collapse of efficient and inefficient subglacial hydrologic networks throughout the year.