A New Weighting Method by Considering the Physical Characteristics of Atmospheric Turbulence and Decorrelation Noise in SBAS-InSAR

Duan, Meng; Xu, Bing; Wu, Wenhao; Cao, Yunmeng; Liu, Jihong; Wang, Guanya; Hou, Jingxin

doi:10.3390/rs12162557

Cited by 15 publications

(7 citation statements)

References 47 publications

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“…7a). The errors due to turbulent atmosphere [38] were corrected based on a previous study [39] (see Fig. 7b).…”

Section: ) Shale Gas Area In Texasmentioning

confidence: 99%

Resolving Three-Dimensional Displacements Caused by Subsurface Fluid Activities From InSAR and Volumetric Strain Model: Adaptive Estimation of Model Parameters

Zhang,

Hu,

Jin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Monitoring the actual surface deformation caused by sub-surface fluid activities is crucial for comprehending the subsurface fluid mutation process. Incorporation of InSAR observations in a prior volume strain model (VSM) can facilitate the estimation of sub-surface fluid volumetric changes and threedimensional surface deformation. However, the model parameters, i.e. the thickness and depth of the sub-surface fluid reservoir, are assumed to be known a priori. Therefore, it is impossible to acquire the precise model parameters in practice. Therefore, we proposed a novel approach for adaptive estimation of the model parameters by utilizing the black hole strategy method integrated with the particle swarm optimization algorithm. First, the performance of this method was tested through simulation experiments. The results suggest that the proposed method can precisely determine the model parameters and threedimensional(3-D) deformations of the sub-surface fluids. Finally, the proposed model was tested using actual data from the Shale gas area in Texas and the Kilauea Caldera volcano in Hawaii, USA. For long-term, slow small deformations or sudden large deformations caused by the sub-surface fluids, the proposed algorithm can obtain corresponding three-dimensional deformations, especially in the presence of incoherent regions, which can still obtain a complete three-dimensional deformation field.

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“…7a). The errors due to turbulent atmosphere [38] were corrected based on a previous study [39] (see Fig. 7b).…”

Section: ) Shale Gas Area In Texasmentioning

confidence: 99%

Resolving Three-Dimensional Displacements Caused by Subsurface Fluid Activities From InSAR and Volumetric Strain Model: Adaptive Estimation of Model Parameters

Zhang,

Hu,

Jin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…This technique enables the identification of stable ground points over time, allowing for the detection of subtle surface deformations indicative of land subsidence. Additionally, small baseline subset (SBAS) analysis was employed to mitigate atmospheric and temporal decorrelation effects, enhancing the accuracy of displacement measurements [72]. Initially, Sentinel-1 images of the study area during 2021-2023 were acquired and preprocessed to remove noise and atmospheric disturbances.…”

Section: Land Subsidencementioning

confidence: 99%

Assessing Riverbank Change Caused by Sand Mining and Waste Disposal Using Web-Based Volunteered Geographic Information

Bayazidy,

Maleki,

Khosravi

et al. 2024

Water

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River water is one of the most important natural resources for economic development and environmental sustainability. However, river water systems are vulnerable in some of the densely populated regions across the globe. Intense sand mining and waste disposal can lead to river changes, loose foundations, and biodiversity loss. This study aims to develop a web-based geographic information system (GIS) platform to monitor river channel changes and their impacts on river environments due to sand mining and waste burial. The system integrates open-source software, Windows Server 2012, a web server, and PostgreSQL with PostGIS plugins for efficient mapping and storage of geospatial data and volunteered reporting of location events. Interferometric methods, including SNAP2STAMPS Automated Algorithm, persistent scatterer interferometry (PSI), small baseline subset (SBAS), and Snap software, were used to analyze spatial changes of subsidence from Sentinel-1 satellite data from 2021 to 2023 in the Gadar River in Oshnavieh, Iran. The results showed that the maximum subsidence at the riverbank was −10.1 cm due to sand mining, and the maximum uplift was 8.2 cm due to waste landfilling. The average subsidence was reported to be −5.1 cm. The results emphasize spatial analysis, showcasing material mining’s impact on subsidence trends and underscoring the significance of public participation in monitoring river health. Three parameters—completeness, correctness, and quality—were used to validate the system. Validation results showed completeness, correctness, and quality rates of 94.15%, 92.48%, and 86.63%, respectively.

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“…Compared with the traditional global navigation satellite system (GNSS) and precise leveling [10,11], the monitoring accuracy of InSAR can reach centimeterlevel, and it has the characteristics of saving time and labour, low cost, and wide monitoring range [12]. However, in the long-term monitoring, InSAR is limited by signal incoherence, which is caused by atmospheric phase error and noise [13], resulting in reduced monitoring accuracy and a loss of ability to monitor tiny deformations. In view of this problem, several scholars improved the phase filtering [14] and phase unwrapping [15] methods in InSAR or established a refined model for a single subsidence basin [16] to improve the monitoring accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…At present, several scholars have proposed new methods to reduce the errors in the SBAS-InSAR monitoring process. For example, Duan et al [13] took incoherent noise and atmospheric turbulence as random variables and proposed a new weighting method to reduce the impact on the monitoring results. Havazli and Wdowinski [22] proposed a method to estimate the detection threshold of SBAS-InSAR to reduce tropospheric delay to improve monitoring accuracy.…”

Section: Introductionmentioning

confidence: 99%

Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR

Wang

Liu

et al. 2022

Journal of Spectroscopy

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Long-term industrial activities tend to cause surface subsidence and damage to ground facilities and local ecological environment. Monitoring and analyzing surface subsidence is of great significance to prevent potential disasters. The surface type of the Yellow River Delta in China is complex and there are many industrial activities, so it is necessary to monitor the surface subsidence in this area. Small Baseline Subset InSAR (SBAS-InSAR) can monitor the surface subsidence with millimeter-level accuracy, but it takes a long time to process wide images (Sentinel-1) and is seriously affected by atmospheric errors. To avoid these limitations, we constructed a method combining the CenterNet network and SBAS-InSAR (CNSBAS-InSAR). Firstly, the CenterNet network is used to automatically detect the subsidence areas from the wide differential interferogram formed by two SAR satellite images and determine the location of the subsidence area. Then, the SBAS-InSAR monitoring is performed on the detected multiple subsidence areas. Finally, the small-scale subsidence results are obtained. In this study, based on 24 Sentinel-1A satellite images acquired from 10 January 2018 to 24 December 2018, nine subsidence areas in Yellow River Delta were detected. Three of them had long-term surface subsidence. They were located in Zhanhua District, Xianhe Town, and Hongguang Village, respectively. This paper focuses on analyzing these three areas. The maximum subsidence rate of Zhanhua District, Xianhe Town, and Hongguang Village were −135.21 mm/a, −330.91 mm/a, and −209.68 mm/a, respectively. In addition, the analysis showed that precipitation in the Zhanhua District could effectively slow down the subsidence rate of the area. The subsidence of Xianhe Town threatened the safety of the Shugang Expressway. The subsidence of Hongguang Village caused the safety risks of buildings. The results of this study prove that CNSBAS-InSAR method is reliable for monitoring subsidence areas and it can provide a reference for local construction and protection of Yellow River Delta.

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A New Weighting Method by Considering the Physical Characteristics of Atmospheric Turbulence and Decorrelation Noise in SBAS-InSAR

Cited by 15 publications

References 47 publications

Resolving Three-Dimensional Displacements Caused by Subsurface Fluid Activities From InSAR and Volumetric Strain Model: Adaptive Estimation of Model Parameters

Resolving Three-Dimensional Displacements Caused by Subsurface Fluid Activities From InSAR and Volumetric Strain Model: Adaptive Estimation of Model Parameters

Assessing Riverbank Change Caused by Sand Mining and Waste Disposal Using Web-Based Volunteered Geographic Information

Detecting, Monitoring, and Analyzing the Surface Subsidence in the Yellow River Delta (China) Combined with CenterNet Network and SBAS-InSAR

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