Surface Displacement Time Series Retrieved by Fully Exploiting Space-Borne SAR Data

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 EO data that are those obtained by an advanced-processing of Synthetic Aperture Radar (SAR) data for monitoring wide areas of the Earth's surface. More specifically, we refer to the SAR technique called advanced differential interferometric synthetic aperture radar (DInSAR) that have demonstrated its capabilities to detect, to map and to analyse the on-going surface displacement phenomena, both spatially and temporally, with centimetre to millimetre accuracy thanks to the generation of deformation maps and time-series. 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.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. 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. OGC makes easier interoperability functionalities, that represent an extremely important aspect because allow the data producers to share geospatial information for all types of cooperative processes, avoiding duplication of efforts and costs. Our implemented GeoNode-Based Platform extends a Geographic Information System (GIS) to a web-accessible resource and adapts the SDI tools to DInSAR-related requirements. Our 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. We demonstrate the high performance of implemented GeoNode-Based Platform, showing DInSAR results relevant to the acquisitions of the Sentinel-1 constellation, collected during 2015-2018 over Italy.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.&#160;&#160;

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

Journal of Glaciology ◽

10.1017/s0022143000001878 ◽

1999 ◽

Vol 45 (150) ◽

pp. 370-383 ◽

Cited By ~ 7

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.

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Sentinel-1 SAR Interferometry for Surface Deformation Monitoring in Low-Land Permafrost Areas

Remote Sensing ◽

10.3390/rs10091360 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1360 ◽

Cited By ~ 22

Author(s):

Tazio Strozzi ◽

Sofia Antonova ◽

Frank Günther ◽

Eva Mätzler ◽

Gonçalo Vieira ◽

...

Keyword(s):

Time Series ◽

Surface Displacement ◽

Deformation Monitoring ◽

In Situ Measurements ◽

River Delta ◽

Sar Interferometry ◽

The Arctic ◽

Lena River ◽

Lena River Delta

Low-land permafrost areas are subject to intense freeze-thaw cycles and characterized by remarkable surface displacement. We used Sentinel-1 SAR interferometry (InSAR) in order to analyse the summer surface displacement over four spots in the Arctic and Antarctica since 2015. Choosing floodplain or outcrop areas as the reference for the InSAR relative deformation measurements, we found maximum subsidence of about 3 to 10 cm during the thawing season with generally high spatial variability. Sentinel-1 time-series of interferograms with 6–12 day time intervals highlight that subsidence is often occurring rather quickly within roughly one month in early summer. Intercomparison of summer subsidence from Sentinel-1 in 2017 with TerraSAR-X in 2013 over part of the Lena River Delta (Russia) shows a high spatial agreement between both SAR systems. A comparison with in-situ measurements for the summer of 2014 over the Lena River Delta indicates a pronounced downward movement of several centimetres in both cases but does not reveal a spatial correspondence between InSAR and local in-situ measurements. For the reconstruction of longer time-series of deformation, yearly Sentinel-1 interferograms from the end of the summer were considered. However, in order to infer an effective subsidence of the surface through melting of excess ice layers over multi-annual scales with Sentinel-1, a longer observation time period is necessary.

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Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation

Remote Sensing ◽

10.3390/rs8030179 ◽

2016 ◽

Vol 8 (3) ◽

pp. 179 ◽

Cited By ~ 13

Author(s):

Yanan Jiang ◽

Mingsheng Liao ◽

Zhiwei Zhou ◽

Xuguo Shi ◽

Lu Zhang ◽

...

Keyword(s):

Time Series ◽

Data Assimilation ◽

Deformation Analysis ◽

Landslide Deformation ◽

Sar Data ◽

Coupling Deformation

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Rice phenology estimation based on statistical models for time-series SAR data

2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) ◽

10.1109/ecticon.2015.7207072 ◽

2015 ◽

Cited By ~ 2

Author(s):

Napat Cota ◽

Teerasit Kasetkasem ◽

Preesan Rakwatin ◽

Thitiporn Chanwimaluang ◽

Itsuo Kumazawa

Keyword(s):

Time Series ◽

Statistical Models ◽

Sar Data ◽

Rice Phenology

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Insights on fault reactivation during the 2019, Mw4.9 Le Teil earthquake in southeastern France, from a joint 3D geology and InSAR study

10.5194/egusphere-egu21-4388 ◽

2021 ◽

Author(s):

Léo Marconato ◽

Philippe-Hervé Leloup ◽

Cécile Lasserre ◽

Séverine Caritg ◽

Romain Jolivet ◽

...

Keyword(s):

Time Series ◽

Surface Displacement ◽

Fault Reactivation ◽

Least Square ◽

Shallow Depth ◽

Slip Distribution ◽

Fault System ◽

Geological Model ◽

Coseismic Slip ◽

3D Geological Model

<div> <div> <div> The 2019, Mw4.9 Le Teil earthquake occurred in southeastern France, causing important damage in a slow deforming region.&#160;Field based, remote sensing and seismological studies following the event revealed its very shallow depth, a rupture length of ~5 km with surface rupture evidences and a thrusting mechanism. We further investigate this earthquake by combining geological field mapping and 3D geology, InSAR time series analysis and coseismic slip inversion. From structural, stratigraphic and geological data collected around the epicenter, we first produce a 3D geological model over a 70 km2 and 3 km deep zone surrounding the 2019 rupture, using the GeoModeller software. This model includes the geometry of the main faults and geological layers, and especially a geometry for La Rouvi&#232;re Fault, an Oligocene normal fault likely reactivated during the earthquake. We also generate a time series of the surface displacement by InSAR, based on Sentinel-1 data ranging from early January 2019 to late January 2020, using the NSBAS processing chain. The spatio-temporal patterns of the surface displacement for this limited time span show neither clear pre-seismic signal nor significant postseismic slip. We extract from the InSAR time series the coseismic displacement pattern, and in particular the along-strike slip distribution that shows spatial variations. The maximum relative displacement along the Line-Of-Sight is up to ~16 cm and is located in the southwestern part of the rupture. We then invert for the slip distribution on the fault from the InSAR coseismic surface displacement field. We use a non-negative least square approach based on the CSI software and the fault surface trace defined in the 3D geological model, exploring the range of plausible fault dip values. Best-fitting dips range between 55&#176; and 60&#176;. Such values are slightly lower than those measured on La Rouvi&#232;re Fault planes in the field. Our model confirms the reactivation of La Rouvi&#232;re fault, with reverse slip at very shallow depth and two main slip patches reaching 30 cm and 24 cm of slip at 400-500m depth. We finally discuss how the 3D fault geometry and geological configuration could have impacted the slip distribution and propagation during the earthquake. This study is a step to better quantify strain accumulation and assess the seismic hazard associated with other similar faults along the C&#233;vennes fault system, in a densely populated area hosting several nuclear plants. </div> </div> </div>

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Land Surface Displacement Study for Kryvyi Rih Mining Area using Radar Interferometry Time Series Analysis

10.3997/2214-4609.201903199 ◽

2019 ◽

Author(s):

S. Stankevich ◽

I. Piestova ◽

O. Titarenko ◽

V. Filipobich ◽

T. Dudar ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Land Surface ◽

Surface Displacement ◽

Mining Area ◽

Radar Interferometry ◽

Series Analysis

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DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

Remote Sensing ◽

10.3390/rs11131619 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1619 ◽

Cited By ~ 3

Author(s):

Zhou Ya’nan ◽

Luo Jiancheng ◽

Feng Li ◽

Zhou Xiaocheng

Keyword(s):

Time Series ◽

High Resolution ◽

Short Term Memory ◽

Multivariate Time Series ◽

Hunan Province ◽

Spatial Features ◽

Optical Images ◽

Multi Temporal ◽

Crop Classification ◽

Sar Data

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

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Farmland Parcel Mapping in Mountain Areas Using Time-Series SAR Data and VHR Optical Images

Remote Sensing ◽

10.3390/rs12223733 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3733

Author(s):

Wei Liu ◽

Jian Wang ◽

Jiancheng Luo ◽

Zhifeng Wu ◽

Jingdong Chen ◽

...

Keyword(s):

Time Series ◽

Time Profile ◽

Classification Method ◽

Guizhou Province ◽

Optical Data ◽

Time Series Classification ◽

Mountainous Areas ◽

Mountain Areas ◽

Actual Distribution ◽

Sar Data

Accurate, timely, and reliable farmland mapping is a prerequisite for agricultural management and environmental assessment in mountainous areas. However, in these areas, high spatial heterogeneity and diversified planting structures together generate various small farmland parcels with irregular shapes that are difficult to accurately delineate. In addition, the absence of optical data caused by the cloudy and rainy climate impedes the use of time-series optical data to distinguish farmland from other land use types. Automatic delineation of farmland parcels in mountain areas is still a very difficult task. This paper proposes an innovative precise farmland parcel extraction approach supported by very high resolution(VHR) optical image and time series synthetic aperture radar(SAR) data. Firstly, Google satellite imagery with a spatial resolution of 0.55 m was used for delineating the boundaries of ground parcel objects in mountainous areas by a hierarchical extraction scheme. This scheme divides farmland into four types based on the morphological features presented in optical imagery, and designs different extraction models to produce each farmland type, respectively. The potential farmland parcel distribution map is then obtained by the layered recombination of these four farmland types. Subsequently, the time profile of each parcel in this map was constructed by five radar variables from the Sentinel-1A dataset, and the time-series classification method was used to distinguish farmland parcels from other types. An experiment was carried out in the north of Guiyang City, Guizhou Province, Southwest China. The result shows that, the producer’s accuracy of farmland parcels obtained by the hierarchical scheme is increased by 7.39% to 96.38% compared with that without this scheme, and the time-series classification method produces an accuracy of 80.83% to further obtain the final overall accuracy of 96.05% for the farmland parcel maps, showing a good performance. In addition, through visual inspection, this method has a better suppression effect on background noise in mountainous areas, and the extracted farmland parcels are closer to the actual distribution of the ground farmland.

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On the Characterization and Forecasting of Ground Displacements of Ocean-Reclaimed Lands

Remote Sensing ◽

10.3390/rs12182971 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2971

Author(s):

Jingzhao Ding ◽

Qing Zhao ◽

Maochuan Tang ◽

Fabiana Calò ◽

Virginia Zamparelli ◽

...

Keyword(s):

Time Series ◽

Ground Deformation ◽

Proper Time ◽

Global Climate ◽

Time Dependent ◽

Ground Displacement ◽

Data Set ◽

Future Evolution ◽

Adopted Model ◽

Sar Data

In this work, we study ground deformation of ocean-reclaimed platforms as retrieved from interferometric synthetic aperture radar (InSAR) analyses. We investigate, in particular, the suitability and accuracy of some time-dependent models used to characterize and foresee the present and future evolution of ground deformation of the coastal lands. Previous investigations, carried out by the authors of this paper and other scholars, related to the zone of the ocean-reclaimed lands of Shanghai, have already shown that ocean-reclaimed lands are subject to subside (i.e., the ground is subject to settling down due to soil consolidation and compression), and the temporal evolution of that deformation follows a certain predictable model. Specifically, two time-gapped SAR datasets composed of the images collected by the ENVISAT ASAR (ENV) from 2007 to 2010 and the COSMO-SkyMed (CSK) sensors, available from 2013 to 2016, were used to generate long-term ground displacement time-series using a proper time-dependent geotechnical model. In this work, we use a third SAR data set consisting of Radarsat-2 (RST-2) acquisitions collected from 2012 to 2016 to further corroborate the validity of that model. As a result, we verified with the new RST-2 data, partially covering the gap between the ENV and CSK acquisitions, that the adopted model fits the data and that the model is suitable to perform future projections. Furthermore, we extended these analyses to the area of Pearl River Delta (PRD) and the city of Shenzhen, China. Our study aims to investigate the suitability of different time-dependent ground deformation models relying on the different geophysical conditions in the two areas of Shanghai and Shenzhen, China. To this aim, three sets of SAR data, collected by the ENV platform (from both ascending and descending orbits) and the Sentinel-1A (S1A) sensor (on ascending orbits), were used to obtain the ground displacement time-series of the Shenzhen city and its surrounding region. Multi-orbit InSAR data products were also combined to discriminate the up–down (subsidence) ground deformation time-series of the coherent points, which are then used to estimate the parameters of the models adopted to foresee the future evolution of the land-reclaimed ground consolidation procedure. The exploitation of the obtained geospatial data and products are helpful for the continuous monitoring of coastal environments and the evaluation of the socio-economical impacts of human activities and global climate change.

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