Synthetic Aperture Radar Technique Research Articles

The Use of Sentinel-1 C-Band Dual Polarimetric and SAR Polarimetry Method for Peatland Analysis in South Kalimantan Indonesia

Abstract Peatlands are a crucial factor in assessing environmental quality due to their capacity to store carbon in their biomass. Tropical peatlands, which account for 11 percent of the world’s total peatland area, are significant terrestrial carbon reservoirs, holding up to 19 percent of the global carbon. Besides carbon storage, peatlands offer numerous ecological benefits to local communities. However, Indonesia’s peat swamp forests are experiencing unprecedented rates of deforestation, drainage, and alteration. Therefore, sustainable management of peatlands through monitoring is essential. Accurate data and information on peatlands are needed for effective land management and use while maintaining hydrological functions. Synthetic Aperture Radar (SAR) is a highly efficient active remote sensing technology compared to optical remote sensing. The Polarimetric SAR method is expected to enhance the effectiveness and efficiency of peatland identification. According to the official website pantaugambut.id, Kalimantan Island has the second-largest peatland area. This research aims to identify peatlands in South Kalimantan, Indonesia, using Sentinel-1 SAR imagery. The study will evaluate the potential of the Polarimetric SAR technique with Sentinel-1 dual polarization for peatland identification. Expected outcomes include identifying peatlands and analyzing their spatial distribution. The research is intended to promote rapid and accurate peatland mapping and serve as a foundational step for global carbon monitoring. The analysis revealed that the unsupervised classification identified 26,376 hectares as peatland, achieving an overall accuracy of 85.7%, precision of 87.5%, recall of 82.4%, and a kappa value of 0.7135.

Utilizing LuTan-1 SAR Images to Monitor the Mining-Induced Subsidence and Comparative Analysis with Sentinel-1

The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments.

A non-contact quantitative risk assessment framework for translational highway landslides: Integration of InSAR, geophysical inversion, and numerical simulation

Landslides frequently disrupt highway networks in mountainous regions globally, presenting a grave threat to the safety of vehicles and pedestrians. A quantitative assessment of landslide risk within the highway network is crucial for the implementation of targeted monitoring, early warning, and engineering interventions. In this study, a non-contact quantitative risk assessment framework for translational landslides is proposed, which integrates Interferometric Synthetic Aperture Radar (InSAR), geophysical inversion, and numerical simulation techniques. The framework can reliably estimate the surface velocity, subsurface geometry, volume, and potential spatial consequences of landslides, and assess the potential damages and losses resulting from the landslide failure. Taking Lashagou L6 landslide in Linxia City, China as a case study, the results demonstrate a robust agreement between the InSAR-derived two-dimensional (2D) displacements and Global Navigation Satellite System (GNSS) observations, which are suitable for the inversion of active landslide thickness. The minimum mean error between the inverted landslide thickness and the observed borehole thickness is 0.8 m. The maximum thickness of the inverted basal sliding surface is 4.1 m, corresponding to a landslide volume of 1.25 × 104 m3. Fully considering the uncertainties within the technical framework, an estimated 1.2–1.7 vehicles are projected to be impacted, resulting in 5.3–7.7 casualties. The proposed non-contact framework demonstrates high reliability and considerable application versatility, particularly beneficial in inaccessible mountainous regions.

Improvement of Coal Mining-Induced Subsidence-Affected (MISA) Zone Irregular Boundary Delineation by MT-InSAR Techniques, UAV Photogrammetry, and Field Investigation

Coal mining-induced ground subsidence is a severe hazard that can damage property, infrastructure, and the environment in the vicinity when the deformation is not negligible. The boundary of a mining-induced subsidence-affected zone refers to the area beyond which the ground subsidence is less concerned. Accurately measuring mining-induced ground deformation is essential for delineating the irregular boundary of the impacted area. This study employs multitemporal interferometric synthetic aperture radar (MT-InSAR) techniques, including differential InSAR (DInSAR), InSAR stacking, and interferometric point target analysis (IPTA), to analyze coal mine subsidence and delineate the boundaries of the mining-impacted zones. DInSAR accurately reconstructs, locates, and detects the trend in mining-induced subsidence and correlates well with documented mining operations. The InSAR stacking method maps the spatial variation of the ground’s average line-of-sight (LOS) velocity over the mining area, delineating the boundary of the impacted zone. IPTA analysis combining multilook and single-pixel phases achieves millimeter-level surface measurement above tunnel alignments and measures unevenly distributed deformation fields. This study considers an average of 4 cm per year of surface deformation in the LOS direction as the subsidence threshold value for delineating the boundary of the mining-induced subsidence-affected (MISA) zone during the active coal mining stage. Interestingly, there are twin transportation tunnels near the mining area. The twin tunnels completed before the coal mining activities started were functioning well, but damage was observed after the mining began. Our study reveals the tunnels are located within the InSAR-derived MISA zone, although the tunnels approach the MISA boundary. As direct signs of subsidence, ground fissures have been identified near the tunnels via field investigations and UAV photogrammetry. Furthermore, the derived distribution of ground fissures validates and verifies InSAR measurements. The integrated approach of MT-InSAR, UVA photogrammetry, and field investigation developed in this study can be applied to delineate the irregular boundary of the MISA zone and study the accumulating effects of mining-induced subsidence on the performance of infrastructure in areas proximate to coal mining activities.

InSAR Analysis of Partially Coherent Targets in a Subsidence Deformation: A Case Study of Maceió

Since the 1970s, extensive halite extraction in Maceió, Brazil, has resulted in significant geological risks, including ground collapses, sinkholes, and infrastructure damage. These risks became particularly evident in 2018, following an earthquake, which prompted the cessation of mining activities in 2019. This study investigates subsidence deformation resulting from these mining operations, focusing on the collapse of Mine 18 on 10 December 2023. We utilized the Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPS-InSAR) technique to analyze a dataset of 145 Sentinel-1A images acquired between June 2019 and April 2024. Our approach enabled the analysis of cumulative displacement, the loss of amplitude stability, the evolution of amplitude time series, and the amplitude change matrix of targets near Mine 18. The study introduces an innovative QPS-InSAR approach that integrates phase and amplitude information using amplitude time series to assess the lifecycle of radar scattering targets throughout the monitoring period. This method allows for effective change detection following sudden events, enabling the identification of affected areas. Our findings indicate a maximum cumulative displacement of −1750 mm, with significant amplitude changes detected between late November and early December 2023, coinciding with the mine collapse. This research provides a comprehensive assessment of deformation trends and ground stability in the affected mining areas, providing valuable insights for future monitoring and risk mitigation efforts.

Evaluating the risk perception due to land subsidence within onyeama mine, South East Nigeria

This paper conducts a systematic evaluation of risk perception due to land subsidence monitored within a major coalmine in Nigeria. There is a general assumption in most hazard research that risk perception should be a first determinant of whether adaptive action is taken or not. To gain a comprehensive insight using land subsidence, we propose a technique that integrates quantitative evaluation (vulnerability assessment) and subjective evaluation (perception analysis) of human responses towards this environmental hazard. Perception of risk, which is dependent on risk magnitude appraisal and risk acceptance, was amplified into the overall risk constellation. Vulnerability assessment was conducted based on the human development index (HDI) at fourteen locations investigated for the study, with particular focus on building networks as an index for loss estimation. We exploit data from Sentinel-1 Synthetic Aperture Radar (SAR) Satellites and Small-Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) technique to map Onyeama Coal Mine in South – East, Nigeria. From the HDI and according to the vulnerability assessment, the margin of error (E) is estimated as 0.06754, with samples proportion of ρˆ= 0.85. The 95 % confidence interval for proportion is in the range of 0.783 to 0.917. The results indicates that with an average of a finite population size of N = 1413 buildings at each study location, about 85 % of the samples (n=100) do not recognize that land subsidence is ongoing and constantly affecting their buildings. As a result of this ignorance and lack of awareness, constantly progressing subsidence becomes normalized in peoples’ perceptions, and their outlook toward danger is not integrated into day-to-day habits. Thus, risk perception is a lesser determinant of mitigation responses towards slowly progressing subsidence, and not actual exposure leading to action.

Deep Learning-based Classification of Breast Tumors using Raw Microwave Imaging Data

Breast cancer is the leading type of malignant neoplasm disease among women worldwide. Breast screening makes extensive use of powerful techniques such as x-ray mammography, magnetic resonance imaging, and ultrasonography. While these technologies have numerous benefits, certain drawbacks such as the use of low-energy ionizing x-rays, a lack of specificity for malignant tissues, and cost, have motivated researchers to investigate novel imaging and detection modalities. Microwave imaging (MWI) has been extensively studied due to its low-cost structure and ability to perform measurements using non-ionizing electromagnetic waves. This study proposes a novel convolutional neural network (CNN) model for detecting and classifying tumor scatterers in MWI simulation data. To accomplish this, 10001 different numerical breast models with tumor scatterers of varying numbers and positions were developed, and the simulation results were derived using the synthetic aperture radar (SAR) technique. The presented CNN structure was trained using 8000 pieces of simulation data, and the remaining data were used for testing, achieving accuracy rates of 99.61% and 99.75%, respectively. The proposed model is compared to three state-of-the-art models on the same dataset in terms of classification performance. The results demonstrate that the proposed model effectively performs effectively well in detecting and classifying tumor scatterers.

Unveiling spatial pattern in slope units-based landslides using a data-driven GAMM and InSAR techniques

ABSTRACT Spatial slope deformation pattern is crucial for landslide early warning and management. Quantitative modelling of the effects of multiple driving factors on the landslide spatial pattern has not yet been fully explored. To address this, this study utilizes data-driven models and Interferometric Synthetic Aperture Radar (InSAR) techniques to unveil the spatial pattern of landslides based on SUs (slope units). Specifically, we propose a methodology that establishes a correlation between InSAR deformation and topographic/meteorological factors by constructing a Generalized Additive Mixed Model (GAMM). Using this method, we analysed ALOS-2 ascending SAR images from April 2020 to September 2021 and successfully obtained cumulative minimum deformation during the period. Through the GAMM, we linked the observed cumulative minimum deformation in each SU to various factors, including topographic and meteorological factors. In terms of accuracy, there is an excellent agreement between the observed and fitted deformation, with a Pearson Correlation Coefficient (PCC) of 0.83. In terms of interpretability, our method can well explain the contribution of each factor to landslide deformation. We conclude that using the data-driven GAMM and InSAR techniques can effectively reveal the spatial pattern of landslides based on SUs. Furthermore, based on the method proposed in this study, it is possible to predict landslide deformation in the future.

A fast synthetic aperture radar echo simulation technique based on ray tracing

A fast synthetic aperture radar echo simulation technique based on ray tracing

A Deep-Learning-Based Algorithm for Landslide Detection over Wide Areas Using InSAR Images Considering Topographic Features.

The joint action of human activities and environmental changes contributes to the frequent occurrence of landslide, causing major hazards. Using Interferometric Synthetic Aperture Radar (InSAR) technique enables the detailed detection of surface deformation, facilitating early landslide detection. The growing availability of SAR data and the development of artificial intelligence have spurred the integration of deep learning methods with InSAR for intelligent geological identification. However, existing studies using deep learning methods to detect landslides in InSAR deformation often rely on single InSAR data, which leads to the presence of other types of geological hazards in the identification results and limits the accuracy of landslide identification. Landslides are affected by many factors, especially topographic features. To enhance the accuracy of landslide identification, this study improves the existing geological hazard detection model and proposes a multi-source data fusion network termed MSFD-Net. MSFD-Net employs a pseudo-Siamese network without weight sharing, enabling the extraction of texture features from the wrapped deformation data and topographic features from topographic data, which are then fused in higher-level feature layers. We conducted comparative experiments on different networks and ablation experiments, and the results show that the proposed method achieved the best performance. We applied our method to the middle and upper reaches of the Yellow River in eastern Qinghai Province, China, and obtained deformation rates using Sentinel-1 SAR data from 2018 to 2020 in the region, ultimately identifying 254 landslides. Quantitative evaluations reveal that most detected landslides in the study area occurred at an elevation of 2500-3700 m with slope angles of 10-30°. The proposed landslide detection algorithm holds significant promise for quickly and accurately detecting wide-area landslides, facilitating timely preventive and control measures.

Mapping vertical and horizonal deformation of the newly reclaimed third runway at Hong Kong International Airport with PAZ, COSMO-SkyMed, and Sentinel-1 SAR images

In order to meet long-term air transportation needs, Hong Kong International Airport (HKIA) has been constructing the third runway system (3RS) through land reclamation projects since 2016. However, the third runway and taxiway, which were paved in September 2021, suffered from severe surface settlement. Therefore, we attempted to reveal the settlement pattern within the 3RS and analyze the factors contributing to this displacement. Employing the multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique, we utilized PAZ, COSMO-SkyMed, and Sentinel-1 satellite images to extract ground displacements along the radar line of sight. The results show that, compared with COSMO-SkyMed and Sentinel-1 images, PAZ demonstrates superior performance in the deformation monitoring of the HKIA, with more deformation details and larger displacements. The maximum settlement rate derived by PAZ on the 3RS exceeds 110 mm/year. To ensure the reliability of the derived displacements along the LOS, we validated them through cross-validation, comparing the outcomes from different SAR data with data from GPS stations. In order to gain a detailed deformation information, we derived the vertical and horizontal (ground range direction) displacements by utilizing deformation measurements along the LOS of multiple SAR satellites and the geometric information, and the maximum values recorded for vertical and E-W displacements reached 88.31 mm/y and 41.91 mm/y, respectively. Furthermore, our investigation revealed that the significant local deformation observed on the taxiway and third runway was predominantly attributed to various factors including the creep of soft soil, consolidation of filling materials, characteristics of road surface materials, and distinct stages of construction.

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 directionsbetween 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predictedby the RFR model using SAR interferometry along two perpendicular profiles confirmedthe model's precision.

Surface Subsidence Characteristics and Causes Analysis in Ningbo Plain by Sentinel-1A TS-InSAR

In recent years, the Ningbo Plain has experienced significant surface subsidence due to urbanization and industrialization, combined with the area’s unique geological and hydrological conditions. To study the surface subsidence and its causes in the Ningbo Plain, this study analyzed 166 scenes of Sentinel-1A SAR images between January 2018 and June 2023. The time series interferometric synthetic aperture radar (TS-InSAR) technique was used to acquire surface subsidence information in the area. The causes of subsidence were analyzed. The results show that: (1) the annual deformation rate of the Ningbo Plain ranges from −44 mm/yr to 12 mm/yr between 2018 and 2023. A total of 15 major subsidence zones were identified by using both the subsidence rate map and optical imagery. The most severe subsidence occurred in the northern industrial park of Cixi City, with a maximum subsidence rate of −37 mm/yr. The study reveals that the subsidence issue in the main urban area has been significantly improved compared to the 2017 subsidence data from the Ningbo Bureau of Natural Resources and Planning. However, three new subsidence areas have emerged in the main urban area, located, respectively, in Gaoqiao Town, Lishe Town, and Qiuyi Village, with maximum rates of −29 mm/year, −24 mm/year, and −23 mm/year, respectively. (2) The causes of subsidence were analyzed using various data, including land use data, geological data, groundwater-monitoring data, and transportation network data. It is found that a strong link exists between changes in groundwater levels, compressible layer thickness, and surface subsidence. The groundwater levels changes and the soft soil layer thickness are the main natural factors causing subsidence in the Ningbo Plain. Additionally, the interaction between static loads from large-scale industrial production and urban construction, along with the dynamic loads from transportation networks, contribute significantly to surface subsidence in the Ningbo Plain. The results from this study enhance the understanding of the driving factors of subsidence in the Ningbo Plain, which can provide necessary guidance for the economic development and decision-making in the region, helping to manage and potentially mitigate future subsidence issues.

Surface deformation monitoring and forecasting of sinabung volcano using interferometry synthetic aperture radar and forest-based algorithm

The Sinabung volcano on Sumatra Island stands out as one of the most active volcanos, having recorded the highest number of eruptions since it resumed activity in 2010. The eruptive activities have caused significant deformations on the volcano's surface. This research aimed to analyze, cluster, and forecast its deformation patterns based on Sentinel-1 A time series data from 2016 to 2023. The differential interferometry synthetic aperture radar (DInSAR) technique was used to monitor monthly deformations and to create time series data. A forest-based forecast (FBF) model was used to predict the rate of changes in volcano surface inflation from January 2024 to December 2027. The deformation times series patterns were also analyzed and clustered into three regions to reveal areas with similar deformation behaviors. The results indicated that Mount Sinabung's deformation is an overall continuous sporadic phenomenon where random ground inflation and deflation were recorded throughout the area with an average deformation rate ranging from 0.06 to 0.32 cm/month and an overall average of 0.197 cm/month with a standard deviation of 0.96 cm, confirming that the volcano is inflating. The highest single-pixel monthly inflation of 4.62 cm was recorded in 2023, while the highest deflation occurred in 2018 at −4.58 cm. The FBF model predicted a gradual and increasing inflationary pattern at the rate of 0.54 cm/month for 2024–2027, higher than the average of the observed data. The deformation within the lava dome and caldera poses a significant risk and could lead to wall collapses and landslides in the crater dome, potentially triggering explosive eruptions. The outcomes of this research serve as valuable supporting information and offer an early warning of potential volcanic disasters in the future.

Detection and Mechanical Interpretation of Surface Displacement by Volcanic and Anthropogenic Interactions around Bandung Basin, through Multi Temporal Interferometric SAR (MT-InSAR) Technique

Abstract This study aims to evaluate capability of MT-InSAR (Multi Temporal interferometric synthetic aperture radar) technique for measuring the surface displacement caused by tectonic, volcanic, and anthropogenic activity around the Bandung Basin, West Java, Indonesia. For this, persistent scatterer (PS), small baseline subset (SBAS), and combined PS and SBAS techniques are adopted. The results reveal different displacement patterns within two areas anthropogenic-caused subsidence in the middle Bandung city and geothermal production field, and volcanic-caused uplift in the northern area around the Tangkuban Parahu volcano. In addition, each InSAR tehnique shows unique spatial deformation. A significant pattern is located around the Wayang Windu geothermal field that large displacements has been occurred around injection, production wells. Those detected displacements are crucial for mitigating geological hazards as well as geothermal production monitoring.

Time-Series Analysis of Mining-Induced Subsidence in the Arid Region of Mongolia Based on SBAS-InSAR

Mongolia’s substantial mineral resources have played a pivotal role in its economic progress, with mining activities significantly contributing to this development. However, these continuous mining operations, particularly at the Oyu Tolgoi copper and gold mine, have induced land subsidence that threatens both production activities and poses risks of geological and other natural disasters. This study employs the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to monitor and analyze time-series surface subsidence using 120 Sentinel-1A datasets from 2018 to 2022. The findings reveal that the SBAS-InSAR method successfully captures the subsidence and its spatial distribution at Oyu Tolgoi, with the maximum cumulative subsidence reaching −742.01 mm and the highest annual average subsidence rate at −158.11 mm/year. Key drivers identified for the subsidence include variations in groundwater levels, active mining operations, and changes in surface stress. This research underscores the ongoing subsidence issue at the Oyu Tolgoi mining area, providing crucial insights that could aid in enhancing mining safety and environmental conservation in the region.

Deep Learning-based DSM Generation from Dual-Aspect SAR Data

Abstract. Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented approach is the first of its kind to implicitly handle the transition from the SAR-specific slant range geometry to a ground-based mapping geometry within the model architecture. Comparative experiments demonstrate the superiority of the dual-aspect fusion over single-image methods in terms of reconstruction quality and geolocation accuracy. Notably, the model exhibits robust performance across diverse acquisition modes and geometries, showcasing its generalizability and suitability for height mapping applications. The study’s findings underscore the potential of deep learning-driven SAR techniques in generating high-quality urban surface models efficiently and economically.

DNN-PolSAR: Urban Image Segmentation and Classification using Polarimetric SAR based on DNNs

Synthetic Aperture Radar (SAR) image segmentation and classification is a popular technique for learning and detection of objects such as buildings, trees, monuments, crops water-bodies, hills, etc. SAR technique is being used for urban development and city-planning, building control of municipal objects, searching best locations, detection of changes in the existing systems, etc. using polarimetry based on Deep Neural Networks. In this paper, weproposed a technique for Urban Image Segmentation and Classification using Polarimetric SAR based on Deep NeuralNetworks (DNN-PolSAR). In our proposed DNN-PolSAR technique, we useMask-RCNN, LinkNet, FPN, and PSP- Net as model architectures, whereas ResNet50, ResNet101, ResNet152, and VGG-19 are used as backbone networks.We first apply polarimetric decomposition on airborne Uninhabited Aerial Vehicle Synthetic Aperture (UAVSAR) im- ages of urban areas and then the decomposed images are fed to DNNs for segmentation and classification. We then simulate DNN-PolSAR considering different hyper-parameters and compare the obtained scores of hyper-parametersagainst used model architectures and backbone networks. In comparison, it is found that DNN-PolSAR based on FPN model with ResNet152 performed the best for segmentation and classification. The mean Average Precision (mAP) score of the DNN-PolSAR based on FPN with a pixel accuracy of 90.9% is 0.823, which outperforms other Deep Learning models.

Monitoring of ground subsidence using PS-InSAR technique in the Southeast Texas (SETX) Region

The southeast Texas (SETX) coastal area, owing to its unique geographical location and geological attributes, is facing a spectrum of geological challenges, such as ground subsidence, flooding, and coastal erosion. This study endeavors to evaluate the recent instances of ground subsidence and their associated rates, focusing on comprehending their implications for flooding within SETX. Employing the Persistence Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, this study employs Sentinel-1 SAR satellite data with descending orbit observations spanning from January 2020 to March 2023. Our findings indicate that both the northwestern and eastern regions of Houston have been settled with rates up to 2 cm/year. This settlement trend is consistent with data derived from GPS and groundwater level measurements. This investigation explicitly shows the substantial temporal and spatial variations in subsidence rates, predominantly influenced by localized groundwater extraction due to urbanization (e.g., population growth, land development, etc.). By advocating for the integration of InSAR, GPS, and groundwater measurements, this research aspires to make valuable contributions toward the mitigation of subsidence and flood-related hazards in the SETX area.

Potential Landslide Identification in Baihetan Reservoir Area Based on C-/L-Band Synthetic Aperture Radar Data and Applicability Analysis

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.