scholarly journals Evaluating Short-Term Spatio-Temporal Tropospheric Variability in Multi-Temporal SAR Interferograms Using LES Models

2021 ◽  
Author(s):  
Fengming Hu ◽  
Ramon Hanssen ◽  
Pier Siebesma ◽  
Kevin Helfer
Keyword(s):  
Time Series ◽  
Weather Condition ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
Integration Time ◽  
Tropospheric Delay ◽  
Atmospheric Effects ◽  
Phase Gradient ◽  
Weather Situation ◽  
Spatio Temporal

Atmospheric delay has a significant impact on synthetic aperture radar (SAR) interferometry, inducing spatial phase errors and decorrelation in extreme weather condition. For Low Earth Orbit (LEO) SAR missions, the atmosphere can be considered as being spatio-temporally frozen due to the short integration time. Geosynchronous (GEO) SAR missions, however, have short revisit times and extensive imaging coverage but with a longer integration time. As a result, GEOSAR interferograms can provide continuous deformation monitoring and integrated refractivity for weather forecasting. However, as the troposphere may vary significantly within the integration time, this may lead to a degradation during focusing and decorrelation of the InSAR pair. Here we simulate a time-series refractivity distribution with a high spatio-temporal resolution, for a fair-weather situation using an advanced Large Eddy Simulation (LES) model, to show the spatio-temporal variability of the troposphere on short time scales. Given GEO orbit parameters with different viewing angles along both azimuth and range directions, corresponding time-series of tropospheric interferograms are obtained based on the SAR geometry, and the impacts of different parameters are compared. Tropospheric delay is found to vary rapidly and a lead to phase gradient exceeding one cycle within a few minutes. Yet, for periods of less than ~15 minutes, a frozen-flow approximation may be successful to mitigate atmospheric decorrelation. Consequently, GEOSAR imaging should be iterative to compensate the atmospheric effects.

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

2018 ◽  
Vol 10 (9) ◽  
pp. 1360 ◽  
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.

Detection of slow-moving landslides in large area using InSAR phase-gradients stacking and YOLOv3

2021 ◽  
Author(s):  
Lv Fu ◽  
Teng Wang
Keyword(s):  
Time Series ◽  
Phase Unwrapping ◽  
Western China ◽  
Landslide Monitoring ◽  
Atmospheric Effects ◽  
Mountainous Area ◽  
Human Society ◽  
Large Area ◽  
Phase Gradient ◽  
Slow Moving

<p>Landslide is one of the major geohazards that endangers the human society and threatens the safety of life and properties. In recent years, attentions have been paid to the Synthetic Aperture Radar interferometry (InSAR) for landslide monitoring with many successful applications. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed in a large area because of phase unwrapping errors, troposphere turbulence and vegetation cover. Here we propose a method combining phase-gradient stacking and the widely-used neural network for tiny object detection: You Only Look Once (YOLOv3) to detect slow-moving landslides from large-scale interferograms. Using the time-series Sentinel-1 SAR images acquired since 2016, we develop a burst-based, phase-gradient stacking algorithm to sum up phase gradients along the azimuth and range directions of short-temporal-baseline interferograms. The stacked phase gradients clearly present the characteristics of localized surface deformation, mainly caused by slow-moving landslides, avoiding the errors result of multiple phase unwrapping in time-series analysis and atmospheric effects. We then train the YOLOv3 network with the stacked phase-gradient maps of known landslides to achieve the quick and automatic landslide detection. We apply our method in the middle section of the Yalong River in mountainous area of western China, with an area of 180,000 km<sup>2</sup>. In addition to the slides that have been published in the inventory, we identify many more slow-moving landslides that cannot be detected by traditional time-series InSAR analysis methods. Our results demonstrate the potential usage of the proposed methods for slow-moving landslide detection in large area, which can be applied before the time-consuming time-series InSAR analysis.</p>

scholarly journals Spatio-temporal filtering for determination of common mode error in regional GNSS networks

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Janusz Bogusz ◽  
Maciej Gruszczynski ◽  
Mariusz Figurski ◽  
Anna Klos
Keyword(s):  
Time Series ◽  
Reference Network ◽  
Atmospheric Effects ◽  
Satellite Orbits ◽  
Optimal Size ◽  
Common Mode ◽  
Temporal Behaviour ◽  
Spatio Temporal ◽  
L1 And L2 ◽  
Gnss Networks

AbstractThe spatial correlation between different stations for individual components in the regional GNSS networks seems to be significant. The mismodelling in satellite orbits, the Earth orientation parameters (EOP), largescale atmospheric effects or satellite antenna phase centre corrections can all cause the regionally correlated errors. This kind of GPS time series errors are referred to as common mode errors (CMEs). They are usually estimated with the regional spatial filtering, such as the "stacking". In this paper, we show the stacking approach for the set of ASG-EUPOS permanent stations, assuming that spatial distribution of the CME is uniform over the whole region of Poland (more than 600 km extent). The ASG-EUPOS is a multifunctional precise positioning system based on the reference network designed for Poland. We used a 5- year span time series (2008-2012) of daily solutions in the ITRF2008 from Bernese 5.0 processed by the Military University of Technology EPN Local Analysis Centre (MUT LAC). At the beginning of our analyses concerning spatial dependencies, the correlation coefficients between each pair of the stations in the GNSS network were calculated. This analysis shows that spatio-temporal behaviour of the GPS-derived time series is not purely random, but there is the evident uniform spatial response. In order to quantify the influence of filtering using CME, the norms L1 and L2 were determined. The values of these norms were calculated for the North, East and Up components twice: before performing the filtration and after stacking. The observed reduction of the L1 and L2 norms was up to 30% depending on the dimension of the network. However, the question how to define an optimal size of CME-analysed subnetwork remains unanswered in this research, due to the fact that our network is not extended enough.

A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1355-1371 ◽  
Author(s):  
Bo Chen ◽  
Tianyi Hu ◽  
Zishen Huang ◽  
Chunhui Fang
Keyword(s):  
Time Series ◽  
Water Level ◽  
Deformation Monitoring ◽  
Monitoring Data ◽  
Arch Dams ◽  
Temporal Clustering ◽  
Diagnosis Method ◽  
Spatio Temporal ◽  
Dam Deformation ◽  
Health Diagnosis

The timely analysis of deformation monitoring data and reasonable diagnosis of the structural health are key tasks in dam health monitoring studies. This article presents a spatio-temporal clustering and health diagnosis method for super-high concrete arch dams that uses deformation monitoring data obtained from plumb meters. The spatio-temporal expression of the deformation monitoring data is proposed first by upgrading a punctuated time series to a curved panel time series, including cross-sectional, dam axial, and temporal changing directions. Second, a comprehensive similarity indicator on three aspects, namely, the absolute distance, incremental distance, and growth rate distance, is constructed after a deep discussion on deformation similarity characteristics both temporally and spatially. Next, the temporal clustering method is proposed by keeping the key features, namely, extreme points and turning points, while eliminating extraneous details, namely, noise points. Finally, the optimal spatio-temporal clustering of dam deformation is achieved by designing a multi-scale fuzzy C-means method of data mining and its iterative algorithm. The proposed method is applied to the Jinping-I hydraulic structure, which is the highest concrete arch dam in the world. The clustering results is quite sensitive in different weight coefficients of the comprehensive similarity indicator and clustering numbers of fuzzy C-means method. The dam deformation behaviors on high-water-level, water-falling, and low-water-level periods are analyzed and diagnosed. The advanced version of proposed methods is verified by comparative analysis on dam health diagnosis results obtained from ordinary deformation distribution figures and the spatio-temporal clustering figures. The proposed method will facilitate the recognition of abnormal deformation areas and associated safety diagnoses.

Deep learning facilitated local deformation monitoring with large-scale SAR interferometry

2021 ◽  
Author(s):  
Teng Wang ◽  
Heng Luo ◽  
Zhipeng Wu ◽  
Lv Fu ◽  
Qi Zhang
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Local Deformation ◽  
Deformation Monitoring ◽  
Sar Interferometry ◽  
Localized Deformation ◽  
Sar Images ◽  
Image Stack

<p>SAR interferometry has stepped in the big-data era, particularly with the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. Large amount of Sentinel-1 SAR images has been acquired and archived, allowing for generating thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications still focus on monitoring kilometer-scale local deformation, sparsely distributed in a large area. It is thus not effective to apply the time-series InSAR analysis to the whole image stack, but to focus on areas with deformation. Aiming at this target, we present our recent work built upon deep neural networks to firstly detect localized deformation and then carry on the time-series analysis on small interferogram patches with deformation signals.</p><p>Here, we first introduce our burst-based Sentinel-1 processor, which has been fully paralleled for large-scale InSAR processing. From these interferograms, we adapt and train several deep neural networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. We apply our networks for mining subsidence and landslides monitoring. Comparing with traditional time-series InSAR analysis, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and the propagation of possible unwrapping errors.</p><p>The presented methods introduce artificial intelligence to the time-series InSAR processing chain and make the mission of regularly monitoring localized deformation sparsely distributed in large scale feasible and more efficient. As future work, we can further improve the temporal resolution of InSAR based local deformation monitoring by training networks combining interferograms from C-band and L-band SAR images, which will be available soon from future SAR missions such as NiSAR and LuTan-1.</p>

Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

2021 ◽  
Author(s):  
Carlos A. Severiano ◽  
Petrônio de Cândido de Lima e Silva ◽  
Miri Weiss Cohen ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Energy Systems ◽  
Fuzzy Time Series ◽  
Renewable Energy Systems ◽  
Spatio Temporal

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

scholarly journals Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

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