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>

Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis

2020 ◽  
Vol 16 (2) ◽  
pp. 64-80
Author(s):  
Shiya Wang
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Financial Time Series ◽  
Special Kind ◽  
Future Trend ◽  
Financial Time ◽  
Series Analysis ◽  
Depth Analysis

With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence.

Four years of InSAR time series analysis reveals an unprecedent inventory of active DSGSD in the Western Alps

2021 ◽  
Author(s):  
Pauline André ◽  
Marie-Pierre Doin ◽  
Marguerite Mathey ◽  
Swann Zerathe ◽  
Riccardo Vassallo ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Large Scale ◽  
Western Alps ◽  
Spatial Extent ◽  
European Alps ◽  
Series Analysis ◽  
Gravitational Deformation ◽  
Deformation Patterns ◽  
The Impact

&lt;p&gt;Based on geomorphological criteria, large-scale slow gravitational deformation affecting entire mountain flank, often being referred as Deep-Seated Gravitational Slope Deformation (DSGSD), have been shown to affect most of the reliefs worldwide. For instance in the European Alps, these deformation patterns were identified in several areas such as the Aosta Valley (Martinotti et al., 2011) or the Mercantour massif (Jomard, 2006). DSGSD inventories based on visual interpretation of scarps and field mapping were then compiled (e.g. Crosta et al., 2013) revealing the widespread occurrence of DSGSD. However, many aspects of these large-scale gravitational processes remain unclear and in particular their present-day activity and temporal evolution remain largely unknown.&lt;/p&gt;&lt;p&gt;The present study aims at characterizing the spatial extent of DSGSD, and their velocity, at the scale of Western Alps through InSAR time series analysis using NSBAS processing chain (Doin et al., 2001). We used the whole SAR Sentinel-1 archive, between 2014 and 2018, with an acquisition every 6 days, on an ascending track. The processing was adapted to fit the specific conditions of the Alps (seasonal snow cover, strong local relief, vegetation and strong atmospheric heterogeneities). In particular we implemented a correction using the ERA 5 weather model and we used snow masks in winter allowing to select long temporal baseline interferograms with as little snow as possible. As we specifically aim to study deformation patterns at the scale of valley flanks, an average high-pass filter on moving subwindows has been applied to the interferograms prior to the implementation of time-serie inversions. This step strongly reduced the impact of residual atmospheric delays.&lt;/p&gt;&lt;p&gt;The resulting velocity map in the line of sight (LOS) of the satellite reveals ubiquitous gravitational deformation patterns over the whole Western Alps, with localized patches of moving slopes showing sharp discontinuities with stable surrounding areas. We used radar geometry and InSAR measurement quality factors as indicators to identify the most trusted areas and to extract an inventory of potential DSGSD with their spatial extent. Doing so, we identified more than two thousands slowly deforming areas characterized by LOS velocities from 4 to 20 mm/year. We then compared the geometries of our &amp;#8220;InSAR-detected-deforming-slopes&amp;#8221; with previously published DSGSD inventories. Good agreements were found for example in the Aosta valley where most of the deforming areas from our velocity map are falling into the DSGSD outlines of Crosta et al. (2013). Currently, we continue to investigate the potential of this large-scale velocity map for DSGSD understanding and we plan to use artificial intelligence to search for possible generic properties between the detected sites.&lt;/p&gt;

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
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