Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65540
Title: Towards slow-moving landslide monitoring by integrating multi-sensor InSAR time series datasets : the Zhouqu case study, China
Authors: Sun, Q
Hu, J
Zhang, L
Ding, X
Keywords: 3D deformations
Geometric distortion
InSAR
Landslides
Multi-sensor
Zhouqu
Issue Date: 2016
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, 2016, v. 8, no. 11, 908 How to cite?
Journal: Remote sensing 
Abstract: Although the past few decades have witnessed the great development of Synthetic Aperture Radar Interferometry (InSAR) technology in the monitoring of landslides, such applications are limited by geometric distortions and ambiguity of 1D Line-Of-Sight (LOS) measurements, both of which are the fundamental weakness of InSAR. Integration of multi-sensor InSAR datasets has recently shown its great potential in breaking through the two limits. In this study, 16 ascending images from the Advanced Land Observing Satellite (ALOS) and 18 descending images from the Environmental Satellite (ENVISAT) have been integrated to characterize and to detect the slow-moving landslides in Zhouqu, China between 2008 and 2010. Geometric distortions are first mapped by using the imaging geometric parameters of the used SAR data and public Digital Elevation Model (DEM) data of Zhouqu, which allow the determination of the most appropriate data assembly for a particular slope. Subsequently, deformation rates along respective LOS directions of ALOS ascending and ENVISAT descending tracks are estimated by conducting InSAR time series analysis with a Temporarily Coherent Point (TCP)-InSAR algorithm. As indicated by the geometric distortion results, 3D deformation rates of the Xieliupo slope at the east bank of the Pai-lung River are finally reconstructed by joint exploiting of the LOS deformation rates from cross-heading datasets based on the surface-parallel flow assumption. It is revealed that the synergistic results of ALOS and ENVISAT datasets provide a more comprehensive understanding and monitoring of the slow-moving landslides in Zhouqu.
URI: http://hdl.handle.net/10397/65540
EISSN: 2072-4292
DOI: 10.3390/rs8110908
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