An Automated Feature Tracking Based Algorithm for Estimation of Ice Flow and Glacier Surface Velocity Using Resourcesat-2A Data for Himalayan Terrain

Determination of ice flow and glacier surface velocity plays a pivotal role in the study of glacial geography, its sensitivity to climate change and its impact on water cycle of the region. This study becomes of utmost importance in case of Himalayan Cryosphere, which is a source of water to many areas in Asia. In this paper, we propose an algorithm for optical remote sensing multispectral data which can automatically calculate the ice flow velocity and further help other glacier related studies. An ortho-rectified image pair of Resourcesat - 2A LISS-3 (Linear Imaging and Self Scanning) sensor is taken as an input, which is further subjected to a cloud mask and snow feature enhancement technique to improve the visibility of the requisite area. This in turn is used as an input to the first level Principal component analysis (PCA). Further a common raster is extracted and then moving chip based sub-pixel correlation is performed to find the ice shift. Post processing steps are used to remove spurious matches and false positives. In the analysis we observe variations in velocities during the winter and summer season, further fluctuations were also observed in velocities for different zones of glaciers. Average glacier surface velocities ranging from 30my
⁻¹
to 110my
⁻¹
are observed. This study shows that optical image based correlation is suitable and efficient for measurement of ice flow in the fast moving glaciers of the Himalayas.