  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.
