Evaluating ScanSAR Interferometry Deformation Time Series Using Bursted Stripmap Data

2011 ◽  
Vol 49 (6) ◽  
pp. 2335-2342 ◽  
Author(s):  
Sean M. Buckley ◽  
Krishnavikas Gudipati
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Radar Interferometry ◽  
Synthetic Aperture ◽  
Image Pair ◽  
Sar Images ◽  
Interferometric Phase ◽  
Burst Pattern ◽  
Burst Synchronization ◽  
Small Baseline

We demonstrate scanning synthetic aperture radar (ScanSAR) advanced radar interferometry processing for surface deformation time series analysis. We apply the small baseline subsets (SBAS) technique to ScanSAR data synthesized from 40 ERS-1 and ERS-2 stripmap SAR images over known deformation in Phoenix, Arizona. The strategy is to construct a burst pattern similar to Envisat ScanSAR data for two scenarios, namely, an idealized 100% burst overlap case and a realistic variable-burst synchronization case in which any image pair has at least 50% burst overlap. We And this latter scenario to be reasonable based on an assessment of the effect of burst overlap on Phoenix interferometric phase coherence. The differences between the variable burst overlap ScanSAR and stripmap SAR SBAS-derived pixel velocities have a mean of 0.02 cm/year and a standard deviation of 0.02 cm/year. It is noted that one can expect SBAS velocity and displacement one-sigma errors of 0.1 cm/year and 0.5 cm, respectively, from multilooked stripmap data. We observe that 96% and 99% of the variable burst overlap ScanSAR pixel velocities are within ±0.1 and ±0.2 cm/year (one- and two-sigma), respectively, of our stripmap SAR pixel velocities. These results are similar to those reported for SBAS analysis applied to low-resolution multilook interferograms derived from coherence-preserving down sampling of stripmap data. We also And that the rms deviations between variable burst overlap ScanSAR and stripmap SBAS displacement estimates are 0.40 ± 0.30 cm. 68% and 94% of the variable burst overlap ScanSAR pixel displacements are within ±0.5 and ±1.0 cm, respectively, of the stripmap displacements.

scholarly journals Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

2014 ◽  
Vol 14 (7) ◽  
pp. 1835-1841 ◽  
Author(s):  
A. Manconi ◽  
F. Casu ◽  
F. Ardizzone ◽  
M. Bonano ◽  
M. Cardinali ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Southern Italy ◽  
Surface Deformation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Main Road ◽  
Rapid Mapping ◽  
X Band ◽  
Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

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.

scholarly journals Time-Series InSAR Analysis of the Small Baseline Subset to Estimate Surface Deformation at Mt. Bromo Indonesia

Teknik ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 126
Author(s):  
Arliandy Pratama Arbad ◽  
Wataru Takeuchi ◽  
Yosuke Aoki ◽  
Achmad Ardy ◽  
Mutiara Jamilah
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Sar Interferometry ◽  
Alos Palsar ◽  
Small Baseline Subset ◽  
Sar Data ◽  
Small Baseline

Penginderaan jauh kini memainkan peranan penting dalam pengamatan perilaku gunung api. Penelitian ini bertujuan untuk mengamati deformasi permukaan Gunung Bromo, yang terletak di Jawa bagian Timur, Indonesia, yang masuk dalam rangkaian sistem volkanik di Taman Nasional Bukit Tengger Semeru (TNBTS). Penggunaan algoritma SAR Interferometry (InSAR) yang disebut sebagai pendekatan Small Baseline Subset (SBAS) memungkinkan perancangan peta kecepatan deformasi rata-rata dan and peta time series displacement di wilayah kajian. Teknik SBAS yang biasa menghasilkan rangkaian observasi tahap interferometrik. Ini tercatat sebagai kombinasi linear dari nilai fase SAR  scene untuk setiap pixel secara tersendiri. Analisis yang dilakukan terutama berdasarkan 22 data SAR data yang diperoleh melalui sensor ALOS/PALSAR selama kurun waktu 2007–2011. Beberapa penelitian menunjukkan bahwa kemampuan analisis InSAR dalam menyelidiki siklus gunung api, terutama Gunung Bromo yang memiliki karakteristik erupsi stratovolcano dalam satu hingga lima tahun. Analisis hasil memperlihatkan adanya kemajuan dari kajian sebelumnya akan InSAR wilayah tersebut, yang lebih fokus  kepada deformasi yang berpengaruh kepada kaldera. Hal ini menunjukkan bahwa penelitian ini bisa diimplementasikan pada manajemen risiko atau manajemen infrastruktur

scholarly journals Deriving Centimeter-Level Coseismic Deformation and Fault Geometries of Small-To-Moderate Earthquakes From Time-Series Sentinel-1 SAR Images

2021 ◽  
Vol 9 ◽  
Author(s):  
Heng Luo ◽  
Teng Wang ◽  
Shengji Wei ◽  
Mingsheng Liao ◽  
Jianya Gong
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Active Tectonics ◽  
Tibet Plateau ◽  
Coseismic Deformation ◽  
Earthquake Catalog ◽  
Sar Images ◽  
Weak Surface ◽  
Phase Screens ◽  
Atmospheric Delays

Small-to-moderate earthquakes (e.g. ≤Mw5.5) occur much more frequently than large ones (e.g. >Mw6.0), yet are difficult to study with InSAR due to their weak surface deformation that are severely contaminated by atmospheric delays. Here we propose a stacking method using time-series SAR images that can effectively suppress atmospheric phase screens and extract weak coseismic deformation in centimeter to sub-centimeter level. Using this method, we successfully derive coseismic surface deformations for three small-to-moderate (Mw∼5) earthquakes in Tibet Plateau and Tienshan region from time-series Sentinel-1 SAR images, with peak line-of-sight deformation ranging from 5–6 mm to 13 mm. We also propose a strategy to downsample interferograms with weak deformation signal based on quadtree mesh obtained from preliminary slip model. With the downsampled datasets, we invert for the centroid locations, fault geometries and slips of these events. Our results demonstrate the potential of using time-series InSAR images to enrich earthquake catalog with geodetic observations for further study of earthquake cycle and active tectonics.

An extended GeoNode-Based Platform for Detailed Analysis of the Spatial/Temporal DInSAR Information Contents

2020 ◽  
Author(s):  
Adele Fusco ◽  
Sabatino Buonanno ◽  
Giovanni Zeni ◽  
Michele Manunta ◽  
Maria Marsella ◽  
...  
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Spatial Data ◽  
High Performance ◽  
Surface Deformation ◽  
Surface Displacement ◽  
Synthetic Aperture ◽  
Sar Data ◽  
Informative Content ◽  
Aperture Radar

<p>We present an efficient tool for managing, visualizing, analysing, and integrating with other data sources, Earth Observation (EO) data for the analysis of surface deformation phenomena. In particular, we focused on specific <span>E</span><span>O</span> data that are those obtained by an <span>a</span><span>dvanced</span>-processing of Synthetic Aperture Radar (SAR) data for monitoring wide areas of the Earth's surface. More specifically, <span>we refer to the </span><span>SAR technique called </span><span>advanced differential interferometric synthetic aperture radar </span><span>(</span><span>DInSAR</span><span>)</span> <span>that </span><span>have demonstrated </span><span>its</span><span> capabilit</span><span>ies</span><span> to detect, </span><span>to </span><span>map and </span><span>to </span><span>analyse the on-going surface displacement phenomena, </span><span>both spatially and temporally, </span><span>with centimetre to millimetre accuracy t</span><span>hanks to the</span><span> generat</span><span>ion of</span><span> deformation maps and time-series</span>. Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, now with a massive and ever-increasing data flow supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program.</p><p align="justify"><span>Considering this big picture, the Spatial Data Infrastructures (SDI) becomes a fundamental tool to implement a framework to handle the informative content of geographic data. Indeed, an SDI represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data. </span></p><p align="justify"><span>We implemented an SDI, extending the functionalities of GeoNode, which is a web-based platform, providing an open-source framework based on the Open Geospatial Consortium (OGC) standards. </span><span>OGC</span> <span>makes easier</span><span> interoperability functionalities, </span><span>that represent an extremely important </span><span>aspect because allow the data producers to share geospatial information for all types of cooperative processes, avoiding duplication of efforts and costs. Our </span><span>implemented</span><span> GeoNode-Based Platform </span><span>extends a Geographic Information System (GIS) to a web-accessible resource and </span><span>adapt</span><span>s the SDI tools </span><span>to DInSAR-related requirements. </span></p><p align="justify"><span>O</span><span>ur efforts have been dedicated to enabling the GeoNode platform to effectively analyze and visualize the spatial/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. </span><span>W</span><span>e </span><span>demonstrate the high performance of implemented</span><span> GeoNode-Based Platform, </span><span>showing </span><span>DInSAR results relevant to the acquisitions of the Sentinel-1 constellation, collected during 2015-2018 </span><span>over Italy</span><span>.</span></p><p align="justify">This work is supported by the 2019-2021 IREA CNR and Italian Civil Protection Department agreement; the H2020 EPOS-SP project (GA 871121); the I-AMICA (PONa3_00363) project; and the IREA-CNR/DGSUNMIG agreement.</p><p> </p><p> </p>

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