Sentinel-1 InSAR-derived land subsidence assessment along the Texas Gulf Coast

Qiao, Xiaojun; Chu, Tianxing; Tissot, Philippe; Holland, Seneca

doi:10.1016/j.jag.2023.103544

Cited by 7 publications

(14 citation statements)

References 30 publications

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“…The spatial resolution is 5 m in the range direction and 20 m in the azimuth direction. The data can be freely acquired at https://search.asf.alaska.edu/ (accessed on 19 May 2024) [49,50]. The short repeat observation cycle and high resolution make Sentinel-1A a reliable data source for studying long-term deformation.…”

Section: Datasetsmentioning

confidence: 99%

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

He,

Yang,

et al. 2024

Remote Sensing

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In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction.

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Section: Datasetsmentioning

confidence: 99%

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

He,

Yang,

et al. 2024

Remote Sensing

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“…• DEM, TWI, VNL, and DTF: Prior studies utilized these features for mapping subsidence susceptibility, with some being contributing factors to the model prediction [35], [36]. • IMPV and VNL: Subsidence in Texas may also be influenced by some other natural factors such as fluctuations in soil moisture during dry periods [37], [38] and human activities like uncontrolled gas flaring and operations related to hydrocarbon extraction, particularly at night in unpopulated areas [27]. IMPV and VNL may provide insights into explaining subsidence.…”

Section: Datamentioning

confidence: 99%

“…3 [27]. Detailed information about PS-InSAR data processing, including the selection of reference points and cGNSS stations for calibration, can be found in [27].…”

Section: A Insar Processingmentioning

confidence: 99%

“…However, the data of hydrocarbon extraction were aggregated at the county level [24], [25], posing challenges in attribution due to the spatial variability inherent in processes related to both land subsidence and anthropogenic activities. Further, even when accounting for various potential features such as hydrocarbon production, groundwater withdrawal, and geological faults, these features were typically examined in isolation [23], [24], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

“…Leveraging large-scale subsidence estimates derived across the Texas Gulf Coast using the PS InSAR technique [27], this study aimed to interpret land subsidence by identifying potential contributors or correlates of the observed VLM within a quantitative model framework. To achieve this, the study applied ML-based regression to establish relations between observed land subsidence and a range of potential features.…”

Section: Introductionmentioning

confidence: 99%

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Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Qiao,

Chu,

Krell

et al. 2024

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

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Subsidence, the downward vertical land motion (VLM), plays a pivotal role in contributing to the risk of coastal flooding. Accurately estimating VLM and identifying its potential features related to subsidence can provide crucial information for stakeholders to make better informed decisions. This study aimed to estimate large-scale subsidence at the Texas Gulf Coast and identify potential subsidence features using explainable models. Nine potential features were considered for modeling the VLM, ranging from natural terrain variations to anthropogenic activities. These features were used to train a random forest (RF) machine learning (ML) model. Explainable artificial intelligence (XAI) techniques including SHapley Additive exPlanations (SHAP) and impurity-and permutation-based feature importance were used to identify the contributions to subsidence. The results demonstrated favorable performance of the RF model, achieving an R 2 value of 0.56 during validation. XAI results underscored the significance of the digital elevation model (DEM) on explaining subsidence at the Texas Coast. Additionally, XAI analysis highlighted the overall contribution of subsidence from anthropogenic activities, such as hydrocarbon extraction and groundwater withdrawal. Furthermore, the sample-level SHAP map provided detailed and reasonable subsidence-attribution results across the study area, showing potentials for automatic and data-driven explanations of the VLM.

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Interpretation techniques to explain the output of a spatial land subsidence hazard model in an area with a diverted tributary

Seihani,

Gholami,

Esmaeilpour

et al. 2024

Applied Computing and Geosciences

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Sentinel-1 InSAR-derived land subsidence assessment along the Texas Gulf Coast

Cited by 7 publications

References 30 publications

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

Triggering of Land Subsidence in and Surrounding the Hangjiahu Plain Based on Interferometric Synthetic Aperture Radar Monitoring

Interpretation and Attribution of Coastal Land Subsidence: An InSAR and Machine Learning Perspective

Interpretation techniques to explain the output of a spatial land subsidence hazard model in an area with a diverted tributary

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