Abstract
Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching of repeat optical images, are increasingly used to obtain displacement and deformation fields. Strain rates are usually computed in a post-processing step based on the gradients of the measured velocity field. This study explores the potential of automatically and directly computing velocity, rotation and strain rates on Earth surface mass movements simultaneously from the matching positions and the parameters of the geometric transformation models using the least squares matching (LSM) approach. The procedures are exemplified using bi-temporal high resolution satellite and aerial images of glacier flow, rockglacier creep and land sliding. The results show that LSM matches the images and computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviations in the order of 10−4 (one level of significance below the measured values) as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the pixel-precision normalized cross-correlation by over 90% under ideal (simulated) circumstances and by about 25% for real multi-temporal images of mass movements.
Highlights
Remote sensing is highly suited for slope monitoring in inaccessible areas such as high mountains and cold regions where mass movement processes such as glacier flow, permafrost creep and rock sliding are common
This study explored the possibility of automatically and simultaneously computing displacement, strain rates and rotation of Earth surface mass movements from repeat high resolution satellite and aerial optical images
The performance of least squares matching (LSM) with an affine geometric transformation model is evaluated in relation to that of the most widely used algorithm for such purposes, i.e., the normalized cross-correlation (NCC)
Summary
Remote sensing is highly suited for slope monitoring in inaccessible areas such as high mountains and cold regions where mass movement processes such as glacier flow, permafrost creep and rock sliding are common. Repeat optical image matching is used to compute displacements on slope movements within the temporal baseline of the images’ acquisitions [1,2,3,4,5,6]. The most commonly used group of image matching methods, for that purpose, are area-based methods, where intensities of image patches (hereafter referred to as templates), usually of square shape and pre-defined size, are matched using a chosen similarity measure. The template within the search image that maximizes similarity or minimizes dissimilarity (depending on the statistic) with the reference template is considered the most likely match [7]. The Euclidean distance between the positions of the reference and the matching templates quantifies displacement, the horizontal displacement component if orthorectified image data are used. There is a range of possible similarity measures [7,10], the normalized cross-correlation (NCC) is the most widely used due to the normalization that makes it insensitive to differences in brightness and contrast [11]
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