Estimation of time-series surface displacements in Taiwan using interferometric SAR analysis

2021 ◽  
Author(s):  
Shoki Hiranaka ◽  
Kazuya Ishitsuka ◽  
Weiren Lin
Keyword(s):  
Time Series ◽  
Surface Displacements ◽  
Interferometric Sar ◽  
Sar Analysis ◽  
Estimation Of Time

Monitoring of Agricultural Grasslands With Time Series of X-Band Repeat-Pass Interferometric SAR

2016 ◽  
Vol 9 (8) ◽  
pp. 3687-3697 ◽  
Author(s):  
Karlis Zalite ◽  
Oleg Antropov ◽  
Jaan Praks ◽  
Kaupo Voormansik ◽  
Mart Noorma
Keyword(s):  
Time Series ◽  
X Band ◽  
Interferometric Sar

Classification of landslide activity based on spaceborne interferometric SAR at the Moselle Valley, Germany

2021 ◽  
Author(s):  
Andre C. Kalia
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Statistical Tests ◽  
Spatial Density ◽  
Deformation Pattern ◽  
Visual Interpretation ◽  
Landslide Activity ◽  
Spatial Coverage ◽  
Interferometric Sar

<p>Landslide activity is an important information for landslide hazard assessment. However, an information gap regarding up to date landslide activity is often present. Advanced differential interferometric SAR processing techniques (A-DInSAR), e.g. Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) are able to measure surface displacements with high precision, large spatial coverage and high spatial sampling density. Although the huge amount of measurement points is clearly an improvement, the practical usage is mainly based on visual interpretation. This is time-consuming, subjective and error prone due to e.g. outliers. The motivation of this work is to increase the automatization with respect to the information extraction regarding landslide activity.</p><p>This study focuses on the spatial density of multiple PSI/SBAS results and a post-processing workflow to semi-automatically detect active landslides. The proposed detection of active landslides is based on the detection of Active Deformation Areas (ADA) and a subsequent classification of the time series. The detection of ADA consists of a filtering of the A-DInSAR data, a velocity threshold and a spatial clustering algorithm (Barra et al., 2017). The classification of the A-DInSAR time series uses a conditional sequence of statistical tests to classify the time series into a-priori defined deformation patterns (Berti et al., 2013). Field investigations and thematic data verify the plausibility of the results. Subsequently the classification results are combined to provide a layer consisting of ADA including information regarding the deformation pattern through time.</p>

scholarly journals On the Use of Weighted Least-Squares Approaches for Differential Interferometric SAR Analyses: The Weighted Adaptive Variable-lEngth (WAVE) Technique

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1103
Author(s):  
Francesco Falabella ◽  
Carmine Serio ◽  
Giovanni Zeni ◽  
Antonio Pepe
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Weighted Least Squares ◽  
Variable Length ◽  
Mathematical Framework ◽  
Ground Displacement ◽  
Phase Measurements ◽  
Temporal Sampling ◽  
Wave Technique ◽  
Interferometric Sar

This paper concentrates on the study of the Weighted Least-squares (WLS) approaches for the generation of ground displacement time-series through Differential Interferometric SAR (DInSAR) methods. Usually, within the DInSAR framework, the Weighted Least-squares (WLS) techniques have principally been applied for improving the performance of the phase unwrapping operations as well as for better conveying the inversion of sequences of unwrapped interferograms to generate ground displacement maps. In both cases, the identification of low-coherent areas, where the standard deviation of the phase is high, is requested. In this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. In particular, the proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, only, so as to discard the noisy phase measurements. The selected interferometric phase values are then inverted by solving a WLS optimization problem. Noteworthy, the adopted, pixel-dependent selection of the “good” interferograms to be inverted may lead the available SAR data to be grouped into several disjointed subsets, which are then connected, exploiting the Weighted Singular Value Decomposition (WSVD) method. However, in some critical noisy regions, it may also happen that discarding of the incoherent interferograms may lead to rejecting some SAR acquisitions from the generated ground displacement time-series, at the cost of the reduced temporal sampling of the data measurements. Thus, variable-length ground displacement time-series are generated. The mathematical framework of the developed technique, which is named Weighted Adaptive Variable-lEngth (WAVE), is detailed in the manuscript. The presented experiments have been carried out by applying the WAVE technique to a SAR dataset acquired by the COSMO-SkyMed (CSK) sensors over the Basilicata region, Southern Italy. A cross-comparison analysis between the conventional and the WAVE method has also been provided.

Retrieval of historical surface displacements of the Baige landslide from time-series SAR observations for retrospective analysis of the collapse event

2020 ◽  
Vol 240 ◽  
pp. 111695 ◽  
Author(s):  
Menghua Li ◽  
Lu Zhang ◽  
Chao Ding ◽  
Weile Li ◽  
Heng Luo ◽  
...  
Keyword(s):  
Time Series ◽  
Retrospective Analysis ◽  
Surface Displacements

A Methodological Note on the Estimation of Time Series

1981 ◽  
Vol 63 (3) ◽  
pp. 471 ◽  
Author(s):  
Roque B. Fernandez
Keyword(s):  
Time Series ◽  
Estimation Of Time

scholarly journals Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives

2020 ◽  
Vol 12 (9) ◽  
pp. 1364 ◽  
Author(s):  
Dinh HO TONG MINH ◽  
Ramon Hanssen ◽  
Fabio Rocca
Keyword(s):  
Time Series ◽  
Big Data ◽  
Synthetic Aperture Radar ◽  
Radar Interferometry ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Future Perspectives ◽  
Interferometric Sar ◽  
Deformation Measurements ◽  
Over Time

The research and improvement of methods to be used for deformation measurements from space is a challenge. From the previous 20 years, time series Synthetic Aperture Radar (SAR) interferometry techniques have proved for their ability to provide millimeter-scale deformation measurements over time. This paper aims to provide a review of such techniques developed in the last twenty years. We first recall the background of interferometric SAR (InSAR). We then provide an overview of the InSAR time series methods developed in the literature, describing their principles and advancements. Finally, we highlight challenges and future perspectives of the InSAR in the Big Data era.

Monitoring of large-scale landslides in Zongling, Guizhou, China, with improved distributed scatterer interferometric SAR time series methods

Landslides ◽  
2020 ◽  
Vol 17 (8) ◽  
pp. 1777-1795
Author(s):  
Jing Wang ◽  
Chao Wang ◽  
Chou Xie ◽  
Hong Zhang ◽  
Yixian Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Interferometric Sar
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