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>

scholarly journals The Multiple Aperture SAR Interferometry (MAI) Technique for the Detection of Large Ground Displacement Dynamics: An Overview

2020 ◽  
Vol 12 (7) ◽  
pp. 1189 ◽  
Author(s):  
Pietro Mastro ◽  
Carmine Serio ◽  
Guido Masiello ◽  
Antonio Pepe
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Ground Surface ◽  
Surface Displacement ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Signal Spectrum ◽  
Aperture Radar

This work presents an overview of the multiple aperture synthetic aperture radar interferometric (MAI) technique, which is primarily used to measure the along-track components of the Earth’s surface deformation, by investigating its capabilities and potential applications. Such a method is widely used to monitor the time evolution of ground surface changes in areas with large deformations (e.g., due to glaciers movements or seismic episodes), permitting one to discriminate the three-dimensional (up–down, east–west, north–south) components of the Earth’s surface displacements. The MAI technique relies on the spectral diversity (SD) method, which consists of splitting the azimuth (range) Synthetic Aperture RADAR (SAR) signal spectrum into separate sub-bands to get an estimate of the surface displacement along the azimuth (sensor line-of-sight (LOS)) direction. Moreover, the SD techniques are also used to correct the atmospheric phase screen (APS) artefacts (e.g., the ionospheric and water vapor phase distortion effects) that corrupt surface displacement time-series obtained by currently available multi-temporal InSAR (MT-InSAR) tools. More recently, the SD methods have also been exploited for the fine co-registration of SAR data acquired with the Terrain Observation with Progressive Scans (TOPS) mode. This work is primarily devoted to illustrating the underlying rationale and effectiveness of the MAI and SD techniques as well as their applications. In addition, we present an innovative method to combine complementary information of the ground deformation collected from multi-orbit/multi-track satellite observations. In particular, the presented technique complements the recently developed Minimum Acceleration combination (MinA) method with MAI-driven azimuthal ground deformation measurements to obtain the time-series of the 3-D components of the deformation in areas affected by large deformation episodes. Experimental results encompass several case studies. The validity and relevance of the presented approaches are clearly demonstrated in the context of geospatial analyses.

scholarly journals Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 116 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Carina Sobe ◽  
Janik Deutscher ◽  
Mathias Schardt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Data Availability ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Series Data ◽  
Forest Mapping ◽  
Sar Data ◽  
Aperture Radar

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

Co- and Early Postseismic Deformation Due to the 2019 Ridgecrest Earthquake Sequence Constrained by Sentinel-1 and COSMO-SkyMed SAR Data

2020 ◽  
Vol 91 (4) ◽  
pp. 1998-2009 ◽  
Author(s):  
Kang Wang ◽  
Roland Bürgmann
Keyword(s):  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Surface Displacement ◽  
Radar Data ◽  
Satellite System ◽  
Earthquake Sequence ◽  
Synthetic Aperture ◽  
Postseismic Deformation ◽  
Synthetic Aperture Radar Data ◽  
Aperture Radar

Abstract The 2019 Ridgecrest earthquake sequence ruptured a series of conjugate faults in the broad eastern California shear zone, north of the Mojave Desert in southern California. The average spacing between Global Navigation Satellite System (GNSS) stations around the earthquakes is 20–30 km, insufficient to constrain the rupture details of the earthquakes. Here, we use Sentinel-1 and COSMO-SkyMed (CSK) Synthetic Aperture Radar data to derive the high-resolution coseismic and early postseismic surface deformation related to the Ridgecrest earthquake sequence. Line of sight (LoS) Interferometric Synthetic Aperture Radar displacements derived from both Sentinel-1 and CSK data are in good agreement with GNSS measurements. The maximum coseismic displacement occurs near the Mw 7.1 epicenter, with an estimated fault offset of ∼4.5  m on a northwest-striking rupture. Pixel tracking analysis of CSK data also reveals a sharp surface offset of ∼1 m on a second northwest-striking fault strand on which the Mw 6.4 foreshock likely nucleated, which is located ∼2–3  km east of the major rupture. The lack of clear surface displacement across this fault segment during the Mw 6.4 event suggests this fault might have ruptured twice, with more pronounced and shallow slip during the Mw 7.1 mainshock. Both Sentinel-1 and CSK data reveal clear postseismic deformation following the 2019 Ridgecrest earthquake sequence. Cumulative postseismic deformation near the Mw 7.1 epicenter ∼2 months after the mainshock reaches ∼5  cm along the satellites’ LoSs. The observed postseismic deformation near the fault is indicative of both afterslip and poroelastic rebound. We provide data derived in this study in various data formats, which will be useful for the broad community studying this earthquake sequence.

scholarly journals Atmospheric corrections in interferometric synthetic aperture radar surface deformation – a case study of the city of Mendoza, Argentina

2013 ◽  
Vol 35 ◽  
pp. 105-113 ◽  
Author(s):  
S. Balbarani ◽  
P. A. Euillades ◽  
L. D. Euillades ◽  
F. Casu ◽  
N. C. Riveros
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Meteorological Data ◽  
Volcanic Eruptions ◽  
Atmospheric Model ◽  
Time Span ◽  
Synthetic Aperture ◽  
Area Of Interest ◽  
Aperture Radar

Abstract. Differential interferometry is a remote sensing technique that allows studying crustal deformation produced by several phenomena like earthquakes, landslides, land subsidence and volcanic eruptions. Advanced techniques, like small baseline subsets (SBAS), exploit series of images acquired by synthetic aperture radar (SAR) sensors during a given time span. Phase propagation delay in the atmosphere is the main systematic error of interferometric SAR measurements. It affects differently images acquired at different days or even at different hours of the same day. So, datasets acquired during the same time span from different sensors (or sensor configuration) often give diverging results. Here we processed two datasets acquired from June 2010 to December 2011 by COSMO-SkyMed satellites. One of them is HH-polarized, and the other one is VV-polarized and acquired on different days. As expected, time series computed from these datasets show differences. We attributed them to non-compensated atmospheric artifacts and tried to correct them by using ERA-Interim global atmospheric model (GAM) data. With this method, we were able to correct less than 50% of the scenes, considering an area where no phase unwrapping errors were detected. We conclude that GAM-based corrections are not enough for explaining differences in computed time series, at least in the processed area of interest. We remark that no direct meteorological data for the GAM-based corrections were employed. Further research is needed in order to understand under what conditions this kind of data can be used.

scholarly journals Measuring the state and temporal evolution of glaciers in Alaska and Yukon using synthetic-aperture-radar-derived (SAR-derived) 3D time series of glacier surface flow

2021 ◽  
Vol 15 (9) ◽  
pp. 4221-4239
Author(s):  
Sergey Samsonov ◽  
Kristy Tiampo ◽  
Ryan Cassotto
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Flow Velocity ◽  
Temporal Evolution ◽  
Surface Flow ◽  
Synthetic Aperture ◽  
Surface Geometry ◽  
Warming Climate ◽  
Sar Data ◽  
Aperture Radar

Abstract. Climate change has reduced global ice mass over the last 2 decades as enhanced warming has accelerated surface melt and runoff rates. Glaciers have undergone dynamic processes in response to a warming climate that impacts the surface geometry and mass distribution of glacial ice. Until recently no single technique could consistently measure the evolution of surface flow for an entire glaciated region in three dimensions with high temporal and spatial resolution. We have improved upon earlier methods by developing a technique for mapping, in unprecedented detail, the temporal evolution of glaciers. Our software computes north, east, and vertical flow velocity and/or displacement time series from the synthetic aperture radar (SAR) ascending and descending range and azimuth speckle offsets. The software can handle large volumes of satellite data and is designed to work on high-performance computers (HPCs) as well as workstations by utilizing multiple parallelization methods. We then compute flow velocity–displacement time series for glaciers in southeastern Alaska during 2016–2021 and observe seasonal and interannual variations in flow velocities at Seward and Malaspina glaciers as well as culminating phases of surging at Klutlan, Walsh, and Kluane glaciers. On a broader scale, this technique can be used for reconstructing the response of worldwide glaciers to the warming climate using archived SAR data and for near-real-time monitoring of these glaciers using rapid revisit SAR data from satellites, such as Sentinel-1 (6 or 12 d revisit period) and the forthcoming NISAR mission (12 d revisit period).

A phenological object-based approach for rice crop classification using time-series Sentinel-1 Synthetic Aperture Radar (SAR) data in Taiwan

2021 ◽  
Vol 42 (7) ◽  
pp. 2722-2739
Author(s):  
Nguyen-Thanh Son ◽  
Chi-Farn Chen ◽  
Cheng-Ru Chen ◽  
Piero Toscano ◽  
Youg-Sing Cheng ◽  
...  
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Rice Crop ◽  
Synthetic Aperture ◽  
Object Based ◽  
Crop Classification ◽  
Sar Data ◽  
Aperture Radar

scholarly journals Monitoring the Land Subsidence Area in a Coastal Urban Area with InSAR and GNSS

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3181 ◽  
Author(s):  
Bo Hu ◽  
Junyu Chen ◽  
Xingfu Zhang
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Surface Deformation ◽  
Land Reclamation ◽  
Shallow Foundation ◽  
Synthetic Aperture ◽  
Reference Station ◽  
Reclamation Area ◽  
Aperture Radar

In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area.

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