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, XL 
Issue Date: 2016
Source: Remote sensing, Nov. 2016, v. 8, no. 11, 908, p. 1-16
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.
Keywords: 3D deformations
Geometric distortion
InSAR
Landslides
Multi-sensor
Zhouqu
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Remote sensing 
EISSN: 2072-4292
DOI: 10.3390/rs8110908
Rights: © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commo
The following publication Sun, Q., Hu, J., Zhang, L., & Ding, X. L. (2016). Towards slow-moving landslide monitoring by integrating multi-sensor InSAR time series datasets : the Zhouqu case study, China. Remote Sensing, 8(11), (Suppl. ), 908, - is available athttps://dx.doi.org/10.3390/rs8110908
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Sun_Multi-sensor_InSAR_Zhouqu.pdf19.4 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Full Text

SCOPUSTM   
Citations

20
Last Week
0
Last month
Citations as of Aug 19, 2020

WEB OF SCIENCETM
Citations

14
Last Week
0
Last month
Citations as of Oct 20, 2020

Page view(s)

139
Last Week
5
Last month
Citations as of Oct 20, 2020

Download(s)

7
Citations as of Oct 20, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.