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.

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 Adjacent-Track InSAR Processing for Large-Scale Land Subsidence Monitoring in the Hebei Plain

2021 ◽  
Vol 13 (4) ◽  
pp. 795
Author(s):  
Xi Li ◽  
Li Yan ◽  
Lijun Lu ◽  
Guoman Huang ◽  
Zheng Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Large Scale ◽  
Synthetic Aperture ◽  
Hebei Plain ◽  
Deformation Velocity ◽  
Large Scale Land ◽  
Subsidence Monitoring ◽  
Aperture Radar

Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km2). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls.

scholarly journals Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1256
Author(s):  
Haonan Jiang ◽  
Timo Balz ◽  
Francesca Cigna ◽  
Deodato Tapete
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Rapid Development ◽  
Soft Soil ◽  
Central China ◽  
Synthetic Aperture ◽  
Non Linear ◽  
Subsidence Curve ◽  
Aperture Radar

Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities.

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 A Novel Method of Generating Deformation Time-Series Using Interferometric Synthetic Aperture Radar and Its Application in Mexico City

2018 ◽  
Vol 10 (11) ◽  
pp. 1741 ◽  
Author(s):  
Xiaying Wang ◽  
Qin Zhang ◽  
Chaoying Zhao ◽  
Feifei Qu ◽  
Juqing Zhang
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Mexico City ◽  
Land Subsidence ◽  
Functional Model ◽  
Synthetic Aperture ◽  
Economic Losses ◽  
Deformation Pattern ◽  
Interferometric Synthetic Aperture Radar ◽  
Aperture Radar

As a result of rapid societal development and urbanization, the pumping of groundwater has gradually increased. Land subsidence has thus become a common geological disaster, which can result in huge economic losses. Interferometric synthetic aperture radar (InSAR), with its large-scale and high-accuracy monitoring characteristics, can attain information on Earth surface deformation using the interferometric phase between couples of SAR images acquired at different times. Time-series results for the ground surface are the key information required to understand the deformation pattern and further study the reason for the subsidence. However, in recent research, most methods for resolving time-series deformation—like the Berardino method—that use residuals in functional model solving and distinguish high-pass displacement and the atmospheric component by filtering do not generally work well and functional models focusing on prior information in the time-series solution process are not always available. In this paper, to solve the above problems, 34 Sentinel-1A descending mode scenes of Mexico City captured between 2015/04/13 and 2016/09/10 are used as experimental data. Firstly, a new functional model is provided to obtain the deformation time-series. The nonlinear deformation and atmospheric phase are combined as an unknown parameter and the method of singular value decomposition (SVD) is used to solve this variable. The nonlinear displacement and atmospheric phase are then separated by the singular spectrum analysis (SSA) method. Finally, the total land subsidence time-series is obtained by adding together the linear displacement and nonlinear displacement. Two typical methods and the proposed method were compared using both unit weights and adaptive weights. The experimental results show that the proposed method can obtain a more accurate time-series deformation result. Moreover, the different weights do not result in significant differences and the solved atmospheric and nonlinear phases have good consistency with the interferogram phase.

scholarly journals Sentinel-1 for Monitoring Land Subsidence of Coastal Cities in Africa Using PSInSAR: A Methodology Based on the Integration of SNAP and StaMPS

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 124 ◽  
Author(s):  
Fabio Cian ◽  
José Blasco ◽  
Lorenzo Carrera
Keyword(s):  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Land Surface ◽  
Surface Deformation ◽  
Climate Adaptation ◽  
Synthetic Aperture ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Land Deformation ◽  
Aperture Radar

The sub-Saharan African coast is experiencing fast-growing urbanization, particularly around major cities. This threatens the equilibrium of the socio-ecosystems where they are located and on which they depend: underground water resources are exploited with a disregard for sustainability; land is reclaimed from wetlands or lagoons; built-up areas, both formal and informal, grow without adequate urban planning. Together, all these forces can result in land surface deformation, subsidence or even uplift, which can increase risk within these already fragile socio-ecosystems. In particular, in the case of land subsidence, the risk of urban flooding can increase significantly, also considering the contribution of sea level rise driven by climate change. Monitoring such fast-changing environments is crucial to be able to identify key risks and plan adaptation responses to mitigate current and future flood risks. Persistent scatterer interferometry (PSI) with synthetic aperture radar (SAR) is a powerful tool to monitor land deformation with high precision using relatively low-cost technology, also thanks to the open access data of Sentinel-1, which provides global observations every 6 days at 20-m ground resolution. In this paper, we demonstrate how it is possible to monitor land subsidence in urban coastal areas by means of permanent scatterer interferometry and Sentinel-1, exploiting an automatic procedure based on an integration of the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatterers (StaMPS). We present the results of PSI analysis over the cities of Banjul (the Gambia) and Lagos (Nigeria) showing a comparison of results obtained with TerraSAR-X, Constellation of Small Satellites for the Mediterranean Basin Observation (COSMO-SkyMed) and Environmental Satellite advanced synthetic aperture radar (Envisat-ASAR) data. The methodology allows us to highlight areas of high land deformation, information that is useful for urban development, disaster risk management and climate adaptation planning.

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 Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3627
Author(s):  
Wahyu Hakim ◽  
Arief Achmad ◽  
Chang-Wook Lee
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Logistic Regression ◽  
Synthetic Aperture Radar ◽  
Land Subsidence ◽  
Multilayer Perceptron ◽  
Synthetic Aperture ◽  
Susceptibility Map ◽  
Vertical Deformation ◽  
Aperture Radar

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.

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