Estimation of land displacement in East Baton Rouge Parish, Louisiana, using InSAR: Comparisons with GNSS and machine learning models

Subsidence in southeastern Louisiana is a significant geological issue caused by natural and human-induced factors like low-lying topography and groundwater pumping. Human activities also led to coastal land loss and reduced sediment supply. Satellite-based technologies such as Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are used to monitor subsidence. Louisiana has about 130 continuously operating reference stations (CORS) monitoring subsidence statewide. GNSS provides accurate point measurements but limited spatial coverage. InSAR, however, detects ground deformation over large areas using satellite-based radar imagery. In response to this advantage, we employed Sentinel-1 SAR images from 2017 to 2021 to estimate the vertical displacement in East Baton Rouge (EBR) Parish. Significant displacement is found in urban and industrial areas, particularly in high- and medium-density residential areas. The significant subsidence area is between Denham Spring and Baton Rouge faults, where residential areas experience displacement of -0.7 to -1 cm/year. The displacement variation in land use indicates significant annual subsidence in some buildings and infrastructure. Three strategic facilities in Baton Rouge Downtown experienced displacement, with -6.1 mm/yr in Downtown, -2.99 mm/yr at Horace Wilkinson Bridge, and -4.94 mm/yr at central railway station. In addition, machine learning is employed to estimate the vertical displacement in the study area. The K-Nearest Neighbors (KNN) model provides a comprehensive understanding of subsidence estimation among the GBR (Gradient Boosting Regression), RFR (Random Forest Regression), and KNN models. Machine learning models revealed that proximity to fault lines and precipitation are the most influential factors in displacement

Assessments of ground subsidence along the railway in the Kashan plain, Iran, using Sentinel-1 data and NSBAS algorithm

The 110-kilometer-long Qom-Kashan railway is one of the busiest lines in Iran, passing through the Kashan plain. The majority of Iran's plains have subsided in recent years as a result of uncontrolled groundwater extraction, and the Kashan plain is no exception. In this study, ground surface displacement in the Kashan plain region and its impact on the railway were investigated using New Small Baseline Subset (NSBAS) in up-down and east–west directions using descending and ascending Sentinel-1 data collected between 2015 and 2021. Our results indicate that the Kashan plain is subsiding more than 90 mm/year. The study of the local areas around the railway which passes through the study area revealed that the rate of vertical velocity in some locations reaches –23 mm/year, while the rate of east–west velocity is insignificant and is approximately ±2 mm/year. Additionally, a method for analyzing the railway's stability based on longitudinal profiles along the railway is presented. Our findings suggest that more than 60% of the railway line is subject to variable amounts of subsidence. Additionally, a region of approximately one kilometer of the railway has been classified as a risk zone due to relatively fast local deformation. After examining the effect of various factors, it was determined that uncontrolled groundwater extraction in agricultural areas contributed to the subsidence in this area. Our results show that the presented stability control approach in this study is highly reliable for creating hazard profiles for linear structures, such as railways

Trends of CO and NO2 Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine

The first case of COVID-19 in Iran was reported on 19 February 2020, 1 month before the Nowruz holidays coincided with the global pandemic, leading to quarantine and lockdown. Many studies have shown that environmental pollutants were drastically reduced with the spread of this disease and the decline in industrial activities. Among these pollutants, nitrogen dioxide (NO2) and carbon monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes in these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three periods from 11 March to 8 April 2019, 2020, and 2021 were investigated. To this end, timeseries of the Sentinel-5P TROPOMI and in situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results of the NO2 derived from Sentinel-5P were in agreement with the in situ data acquired from ground-based stations (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO2 and CO pollutants in 2020 (the first year of the COVID-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules, as well as people’s initial fear of the coronavirus. Contrarily, these pollutants in 2021 (the second year of the COVID-19 pandemic) were higher than those in 2020 by 5%, which could have been due to high vehicle traffic and a lack of serious policy- and law-making by the government to ban urban and interurban traffic. These findings are essential criteria that might be used to guide future manufacturing logistics, traffic planning and management, and environmental sustainability policies and plans. Furthermore, using the COVID-19 scenario and free satellite-derived data, it is now possible to investigate how harmful gas emissions influence air quality. These findings may also be helpful in making future strategic decisions on how to cope with the virus spread and lessen its negative social and economic consequences

Surface displacement measurement and modeling of the Shah-Gheyb salt dome in southern Iran using InSAR and machine learning techniques

Salt domes play a crucial role in hydrocarbon storage, underground construction, solution mining, and mineralization. Therefore, deformation monitoring is essential for analyzing the kinematics and impact of salt domes. This study aims to measure the temporal displacements of the Shah-Gheyb salt dome from 2016 to 2019 and from 2020 to 2022 using the New Small Baseline Subset (NSBAS) Interferometric Synthetic Aperture Radar (InSAR) technique and to predict future displacements through machine learning models. A total of 14 data layers, including topography, remote sensing, hydrology, and geology group were used in Machine Learning (ML). Random Forest Regression (RFR) and Support Vector Regression (SVR) models were employed to project displacements in both the East-West (E-W) and Up-Down (U-D) components through 29 scenarios. In the E-W direction, the salt dome exhibits a displacement rate of 39 mm/year, while in the U-D direction, it varies between −18 and +6 mm/year. ML predictions and SAR interferometry data processing results for the period 2020–2022 were validated using Root Mean Square Error (RMSE) and the correlation coefficient (R). The RFR model demonstrated the lowest RMSE of 1.9 mm for the E-W component, achieving a maximum R-value of 97.3 %. For the U-D component, the RMSE was 2.8 mm, with an R-value of 55.8 %. Evaluation of the predictive performance of the ML models and a comparison of InSAR and ML outcomes indicated that the RFR model predicted displacement along the E-W and U-D directions between 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predicted by the RFR model using SAR interferometry along two perpendicular profiles confirmed the model's precision

Integrated Analysis of Hashtgerd plain deformation, Using Sentinel-1 SAR, Geological and Hydrological Data

Due to its proximity to Tehran, the Hashtgerd catchment in Iran is an important region that has experienced alarming subsidence rates in recent years. This study estimated the ground surface deformation in the Hashtgerd plain between 2015 and 2020 using Sentinel-1 SAR data and InSAR technique. The average LOS displacement of the ascending and descending tracks was -23 cm/year and -22 cm/year, respectively. The central area of the plain experienced the greatest vertical subsidence, with a more than -100 cm cumulative displacement. The Karaj-Qazvin railway and highway that pass through this area have been damaged by subsidence, according to an analysis of profiles drawn along the transportation lines. The southern sections of Hashtgerd city have experienced a total displacement of -30 cm/year over the course of about six years. The relationship between changes in groundwater level and subsidence rate in this region was examined using piezometer and precipitation data. Geoelectric sections and piezometric well logs were also utilized to investigate the geological characteristics of the Hashtgerd aquifer. According to the findings, the leading causes of subsidence were uncontrolled groundwater abstraction. This research highlights the need to comprehend the spatial distribution of confined aquifers and their effect on subsidence, which can aid in the development of a suitable management strategy to restore these aquifers