scholarly journals Satellite radar interferometry time series analysis of surface deformation for Los Angeles, California

2004 ◽  
Vol 31 (23) ◽  
Author(s):  
Riccardo Lanari ◽  
Paul Lundgren ◽  
Mariarosaria Manzo ◽  
Francesco Casu
Keyword(s):  
Time Series ◽  
Los Angeles ◽  
Time Series Analysis ◽  
Surface Deformation ◽  
Radar Interferometry ◽  
Series Analysis ◽  
Satellite Radar

scholarly journals LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor

2020 ◽  
Vol 12 (3) ◽  
pp. 424 ◽  
Author(s):  
Yu Morishita ◽  
Milan Lazecky ◽  
Tim Wright ◽  
Jonathan Weiss ◽  
John Elliott ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Open Source ◽  
Large Scale ◽  
Surface Deformation ◽  
Atmospheric Correction ◽  
Sar Interferometry ◽  
Processing Scheme ◽  
Series Analysis ◽  
Analysis Package

For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit.

scholarly journals TIME SERIES ANALYSIS OF SURFACE DEFORMATION OF BENGALURU CITY USING SENTINEL-1 IMAGES

2018 ◽  
Vol IV-5 ◽  
pp. 473-477
Author(s):  
N. Ittycheria ◽  
D. S. Vaka ◽  
Y. S. Rao
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Deformation ◽  
Linear Trend ◽  
Polarization Data ◽  
Persistent Scatterer Interferometry ◽  
Advanced Technique ◽  
Interferometric Technique ◽  
Series Analysis ◽  
Persistent Scatterers

<p><strong>Abstract.</strong> Persistent Scatterer Interferometry (PSI) is an advanced technique to map ground surface displacements of an area over a period. The technique can measure deformation with a millimeter-level accuracy. It overcomes the limitations of Differential Synthetic Aperture Radar Interferometry (DInSAR) such as geometric, temporal decorrelation and atmospheric variations between master and slave images. In our study, Sentinel-1A Interferometric Wide Swath (IW) mode descending pass images from May 2016 to December 2017 (23 images) are used to identify the stable targets called persistent scatterers (PS) over Bengaluru city. Twenty-two differential interferograms are generated after topographic phase removal using the SRTM 30 m DEM. The main objective of this study is to analyze urban subsidence in Bengaluru city in India using the multi-temporal interferometric technique such as PSI. The pixels with Amplitude Stability Index &amp;geq;<span class="thinspace"></span>0.8 are selected as initial PS candidates (PSC). Later, the PSCs having temporal coherence &amp;gt;<span class="thinspace"></span>0.5 are selected for the time series analysis. The number of PSCs that are identified after final selection are reduced from 59590 to 54474 for VV polarization data and 15611 to 15596 for VH polarization data. It is interesting to note that a very less number of PSC are identified in cross-polarized images (VH). A high number of PSC have identified in co-polarized (VV) images as the vertically oriented urban targets produce a double bounce, which results in a strong return towards the sensor. The velocity maps obtained using VV and VH polarizations show displacement in the range of &amp;plusmn;<span class="thinspace"></span>20<span class="thinspace"></span>mm<span class="thinspace"></span>year<sup>&amp;minus;1</sup>. The subsidence and the upliftment observed in the city shows a linear trend with time. It is observed that the eastern part of Bengaluru city shows more subsidence than the western part.</p>

Automatic Monitoring Method for Surface Deformation of Coastal Area Based on Time Series Analysis

2019 ◽  
Vol 93 (sp1) ◽  
pp. 194
Author(s):  
Lei Liu ◽  
Chao Wang ◽  
Hong Zhang ◽  
Yixian Tang ◽  
Ziwen Zhang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Coastal Area ◽  
Surface Deformation ◽  
Automatic Monitoring ◽  
Monitoring Method ◽  
Series Analysis

Radar interferometry time series analysis of Mashhad subsidence

2009 ◽  
Vol 37 (1) ◽  
pp. 147-156 ◽  
Author(s):  
Maryam Dehghani ◽  
Mohammad Javad Valadan Zoej ◽  
Sassan Saatchi ◽  
Juliet Biggs ◽  
Barry Parsons ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Radar Interferometry ◽  
Series Analysis
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