Comparison of SAR and optical data in deriving glacier velocity with feature tracking

Abstract

Feature tracking is an efficient way to derive glacier velocity. It is based on a cross-correlation algorithm that seeks offsets of the maximal correlation windows on repeated satellite images. In this paper we demonstrate that different window sizes lead to different velocities. The averaged velocity gradient (AVG) method is proposed to improve window sizes in feature tracking and to obtain the most suitable flow field. The AVG method measures velocity variation between adjacent windows on the whole glacier in the image. Different window sizes lead to different AVG values, and the best-size window corresponds to the value where the AVG changes from abrupt to gradual. Using improved feature tracking, two flow fields of the same glacier are acquired with Advanced Land Observing Satellite (ALOS) optical and synthetic aperture radar (SAR) data, respectively. The advantages, application conditions, accuracy and disadvantages of the two kinds of data using the feature tracking method are discussed.