scholarly journals Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-1 SAR Data

1999 ◽  
Vol 45 (150) ◽  
pp. 370-383 ◽  
Author(s):  
Kim Morris ◽  
Shusun Li ◽  
Martin Jeffries
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Large Scale ◽  
Vector Fields ◽  
Lateral Displacement ◽  
Motion Vector ◽  
Sar Interferometry ◽  
East Siberian Sea ◽  
Pack Ice ◽  
Sar Data

Abstract Synthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

scholarly journals Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-1 SAR Data

1999 ◽  
Vol 45 (150) ◽  
pp. 370-383 ◽  
Author(s):  
Kim Morris ◽  
Shusun Li ◽  
Martin Jeffries
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Large Scale ◽  
Vector Fields ◽  
Lateral Displacement ◽  
Motion Vector ◽  
Sar Interferometry ◽  
East Siberian Sea ◽  
Pack Ice ◽  
Sar Data

AbstractSynthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

scholarly journals Ice-shelf dynamics near the front of the Filchner-Ronne Ice Shelf, Antaretica, revealed by SAR interferometry: model/interferogram comparison

1998 ◽  
Vol 44 (147) ◽  
pp. 419-428 ◽  
Author(s):  
Douglas R MacAyeal ◽  
Eric Rignot ◽  
Christina L Hulbe
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Sar Interferometry ◽  
Deformation Pattern ◽  
Ice Shelf ◽  
Mechanical Coupling ◽  
Iceberg Calving ◽  
Shelf Dynamics ◽  
Coastal Boundary ◽  
Shelf Flow

AbstractWe compare European remote-sensing satellite (ERS) synthetic aperture radar interferograms with artificial interferograms constructed using output of a finite-element ice-shelf flow model to study the dynamics of Filchner-Ronne Ice Shelf (FRIS), Antaretica, near Hemmen Ice Rise (HIR) where the iceberg-calving front intersects Berkner Island. We find that the model must account for rifts, mechanically competent sea ice which fills rifts, and ice softening in coastal boundary layers in order to agree with the ice-deformation pattern implied by observed interferograms. Analysis of the stress field in the model experiment that best matches the observed interferograms suggests that: (1) HIR introduces weakness into the ice shelf through the generation of large-scale rifts, and (2) the melange of sea ice and ice-shelf fragments that fills the rifts stabilizes the shelf front by providing mechanical coupling between the fractured shelf front and the adjacent coast. The rift-filling melange could melt more easily than the surrounding ice shelf and thus could represent a vulnerability of the FRIS to climate warming.

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 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 Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice

2021 ◽  
Vol 13 (21) ◽  
pp. 4421
Author(s):  
Stefan Kern
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Correlation Length ◽  
Large Scale ◽  
Total Error ◽  
Grid Cell ◽  
High Concentration ◽  
Sea Ice Concentration ◽  
Length Scales ◽  
Ice Concentration

The European Organisation for the Exploitation of Meteorological Satellites-Ocean and Sea Ice Satellite Application Facility–European Space Agency-Climate Change Initiative (EUMETSAT-OSISAF–ESA-CCI) Level-4 sea-ice concentration (SIC) climate data records (CDRs), named SICCI-25km, SICCI-50km and OSI-450, provide gridded SIC error estimates in addition to SIC. These error estimates, called total error henceforth, comprise a random, uncorrelated error contribution from retrieval and sensor noise, aka the algorithm standard error, and a locally-to-regionally correlated contribution from gridding and averaging Level-2 SIC into the Level-4 SIC CDRs, aka the representativity error. However, these CDRs do not yet provide an error covariance matrix. Therefore, correlation scales of these error contributions and the total error in particular are unknown. In addition, larger-scale SIC errors due to, e.g., unaccounted weather influence or mismatch between the actual ice type and the algorithm setup are neither well represented by the total error, nor are their correlation scales known for these CDRs. In this study, I attempt to contribute to filling this knowledge gap by deriving spatial correlation length scales for the total error and the large-scale SIC error for high-concentration pack ice. For every grid cell with >90% SIC, I derive circular one-point correlation maps of 1000 km radius by computing the cross-correlation between the central 31-day time series of the errors and all other 31-day error time series within that circular area (disc) with 1000 km radius. I approximate the observed decrease in the correlation away from the disc’s center with an exponential function that best fits this decrease and thereby obtain the correlation length scale L sought. With this approach, I derive L separately for the total error and the large-scale SIC error for every high-concentration grid cell, and map, present and discuss these for the Arctic and the Southern Ocean for the year 2010 for the above-mentioned products. I find correlation length scales are substantially smaller for the total error, mostly below ~200 km, than the SIC error, ~200 km to ~700 km, in both hemispheres. I observe considerable spatiotemporal variability of the SIC error correlation length scales in both hemispheres and provide first directions to explain these. For SICCI-50km, I present the first evidence of the method’s robustness for other years and time series of L for 2003–2010.

scholarly journals The Use of Erts Photographs to Measure the Movement and Deformation of Sea Ice

1975 ◽  
Vol 15 (73) ◽  
pp. 429-436
Author(s):  
J. F. Nye
Keyword(s):  
Sea Ice ◽  
Continuum Model ◽  
Large Scale ◽  
Small Scale ◽  
Strain Rates ◽  
Pack Ice ◽  
Ice Velocity ◽  
Movement And Deformation ◽  
Preliminary Study ◽  
Displacement Measurements

AbstractIs it justified to adopt a two-dimensional continuum model for the movement and large-scale deformation of pack ice? A preliminary study oi’ ERTS-1 photography shows that the details of the ice movement are readily measurable; the problem is not in the accuracy of the remote sensing but in the inherent graininess of the sea ice. There is a spatial variation of ice velocity on a scale of several hundreds of kilometres; smaller-scale variations are superimposed on this, but their amplitude is not enough to obscure the large-scale trend. A continuum model is applicable, but, because of the small-scale variations in the velocity of the sea ice itself, it is not meaningful to specify continuum strain-rates on a scale of, say, 100 km to more than a certain accuracy, If ERTS pictures are available during the AIDJEX main experiment they could provide the necessary strain and displacement measurements for comparison with the predictions of the AIDJEX model.

Continental scale SBAS-DInSAR processing for the generation of Sentinel-1 deformation time series within a cloud computing environment: achieved results and lessons learned

2020 ◽  
Author(s):  
Riccardo Lanari ◽  
Manuela Bonano ◽  
Sabatino Buonanno ◽  
Francesco Casu ◽  
Claudio De Luca ◽  
...  
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Surface Deformation ◽  
Stress Test ◽  
Sar Interferometry ◽  
Continental Scale ◽  
Spatial Coverage ◽  
Small Baseline Subset ◽  
Sar Data ◽  
Small Baseline

&lt;p&gt;The Sentinel-1 constellation of the Copernicus Program already represents a big revolution within the Earth Observation (EO) scenario. This result is mainly due to the capability of this constellation to acquire huge volumes of SAR data all over the globe, with a wide spatial coverage, a short revisit time (12 or 6 days in the case of one or two operating satellites, respectively), and a free and open access data policy. In particular, the availability of such a large amount of SAR data acquired through the TOPS mode, characterized by a short &amp;#8220;orbital tube&amp;#8221; (with a 200m nominal diameter) and a specific design for ensuring differential SAR interferometry (DInSAR) applications, has opened the possibility to investigate Earth surface deformation phenomena at unprecedented spatial scale and with a high temporal rate.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Among several advanced DInSAR algorithms, a widely used approach is the Small BAseline Subset (SBAS) technique, which has already proven its effectiveness to investigate surface displacements with centimeter- to millimeter-level accuracy in different scenarios. Moreover, a parallel algorithmic solution for the SBAS approach, referred to as Parallel Small BAseline Subset (P-SBAS), has been recently developed. This approach permits to generate, in an automatic and unsupervised way, advanced DInSAR products by taking full benefit from parallel computing architectures, such as cluster, grid and, above all, cloud computing infrastructures.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;In this work we present the results of a DInSAR experiment, based on the P-SBAS approach, carried out at the European scale. In particular, we exploited the entire available Sentinel-1 dataset collected through the TOPS acquisition mode between March 2015 and September 2018 from descending orbits over large part of Europe. Moreover, the overall analysis wasbcarried out by using the Copernicus Data and Information Access Services (DIAS) and, in particular, those provided by the ONDA DIAS platform, which was selected through a public tender. This activity, carried out as stress test of the EPOSAR service included in the Satellite Data Thematic Core Service of the EPOS infrastructure, permitted to investigate the DIAS capacity to operationally serve systematic and automatic DInSAR processing services, such as the one based on the P-SBAS approach.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Our experiment was successfully completed, allowing the retrieval of the deformation time-series of the overall investigated area with the final products having the main characteristics summarized in the following:&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Exploited Sentinel-1 data: ~72.000&lt;/li&gt; &lt;li&gt;Covered Area: ~4.500.000 km&lt;sup&gt;2&lt;/sup&gt;&lt;/li&gt; &lt;li&gt;Coherent (multilook) SAR pixels: ~120.000.000&lt;/li&gt; &lt;li&gt;Final products pixel dimension: ~80 m&lt;/li&gt; &lt;li&gt;Time elapsed: ~6 months&lt;/li&gt; &lt;/ul&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The presented discussion will highlight the main pros and cons of the exploited solution for such wide area DInSAR experiment. Moreover, the analysis of the achieved results will also show the high quality of the retrieved DInSAR results, that can be of interest for the Solid Earth scientific community, and the potentially positive impact of the presented solution for what concerns the future development of the European Ground Motion Service.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

scholarly journals Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal with multi-source SAR data

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Meng Ao ◽  
Lu Zhang ◽  
Yuting Dong ◽  
Lijun Su ◽  
Xuguo Shi ◽  
...  
Keyword(s):  
Time Series ◽  
Life Cycle ◽  
Deformation Rate ◽  
Source Region ◽  
Sar Interferometry ◽  
Intensity Change ◽  
Alos Palsar ◽  
Active Deformation ◽  
Sar Data ◽  
Post Disaster

Abstract A catastrophic landslide disaster happened on 2 August 2014 on the right bank of Sunkoshi River in Nepal, resulting in enormous casualties and severe damages of the Araniko highway. We collected multi-source synthetic aperture radar (SAR) data to investigate the evolution life cycle of the Sunkoshi landslide. Firstly, Distributed Scatterers SAR Interferometry (DS-InSAR) technology is applied to analyze 20 ALOS PALSAR images to retrieve pre-disaster time-series deformation. The results show that the upper part, especially the top of the landslide, has long been active before collapse, with the largest annual LOS deformation rate more than − 30 mm/year. Time series deformations measured illustrate that rainfall might be a key driving factor. Next, two pairs of TerraSAR-X/TanDEM-X bistatic data are processed to identify the landslide affected area by intensity change detection, and to generate pre- and post-disaster DSMs. Surface height change map showed maximum values of − 150.47 m at the source region and 55.65 m in the deposit region, leading to a debris volume of 5.4785 ± 0.6687 million m3. Finally, 11 ALOS-2 PALSAR-2 and 82 Sentinel-1 SAR images are analyzed to derive post-disaster annual deformation rate and long time series displacements of the Sunkoshi landslide. The results illustrated that the upper part of the landslide were still in active deformation with the largest LOS displacement velocity exceeding − 100 mm/year.

Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-I SAR data

1999 ◽  
Vol 45 (150) ◽  
pp. 370-383 ◽  
Author(s):  
Kim Morris ◽  
Shusun Li ◽  
Martin Jeffries
Keyword(s):  
Sea Ice ◽  
East Siberian Sea ◽  
Sar Data
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