Small Baseline Subset Research Articles

Refined Landslide Susceptibility Mapping by Integrating the SHAP-CatBoost Model and InSAR Observations: A Case Study of Lishui, Southern China

Landslide susceptibility mapping based on static influence factors often exhibits issues of low accuracy and classification errors. To enhance the accuracy of susceptibility mapping, this study proposes a refined approach that integrates categorical boosting (CatBoost) with small baseline subset interferometric synthetic-aperture radar (SBAS-InSAR) results, achieving more precise and detailed susceptibility mapping. We utilized optical remote sensing images, the information value (IV) model, and fourteen influencing factors (elevation, slope, aspect, roughness, profile curvature, plane curvature, lithology, distance to faults, land use type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), distance to rivers, distance to roads, and annual precipitation) to establish the IV-CatBoost landslide susceptibility mapping method. Subsequently, the Sentinel-1A ascending data from January 2021 to March 2023 were utilized to derive the deformation rates within the city of Lishui in the southern region of China. Based on the outcomes derived from IV-CatBoost and SBAS-InSAR, a discernment matrix was formulated to rectify inaccuracies in the partitioned regions, leading to the creation of a refined information value CatBoost integration (IVCI) landslide susceptibility mapping model. In the end, we utilized optical remote sensing interpretations alongside surface deformations obtained from SBAS-InSAR to cross-verify the excellence and accuracy of IVCI. Research findings indicate a distinct enhancement in susceptibility levels across 165,784 grids (149.20 km2) following the integration of SBAS-InSAR correction. The enhanced susceptibility classes and the spectral characteristics of remote sensing images closely correspond to the trends of SBAS-InSAR cumulative deformation, reflecting a high level of consistency with field-based conditions. These improved classifications effectively enhance the refinement of landslide susceptibility mapping. The refined susceptibility mapping approach proposed in this paper effectively enhances landslide prediction accuracy, providing valuable technical reference for landslide hazard prevention and control in the Lishui region.

Monitoring and Analysis of Land Subsidence in Cangzhou Based on Small Baseline Subsets Interferometric Point Target Analysis Technology

Cangzhou is located in the northeast part of the North China Plain; here, groundwater is the main water source for production and living. Due to the serious regional land subsidence caused by long-term overexploitation of groundwater, the monitoring of land subsidence in this area is significant. In this paper, we used the Small Baseline Subsets Interferometric Point Target Analysis (SBAS-IPTA) technique to process the Envisat-ASAR, Radarsat-2, and Sentinel-1A data and obtained the land subsidence of Cangzhou from 2004 to 2020. Additionally, we obtained winter wheat distribution information in Cangzhou using the Pixel Information Expert Engine (PIE-Engine) remote sensing cloud platform. On this basis, we analyzed the relationship between ground water level, winter wheat planting area, and the response of land subsidence according to the land use type and groundwater level monitoring data near the winter wheat growing area. The results show that during 2004–2020, the average annual subsidence rate of many places in Cangzhou was higher than 30 mm/year, and the maximum subsidence rate was 115 mm/year in 2012. From 2004 to 2020, the area of the subsidence funnel showed a trend of first increasing and then decreasing. In 2020, the subsidence funnel area reached 6.9 × 103 km2. The winter wheat planting area in the urban area showed a trend of first decreasing, then increasing and then decreasing, and it accounted for a large proportion in the funnel area. At the same time, we studied the relationship between the land subsidence rate and the water level at different burial depths and the response of winter wheat planting area. The results showed that the change of confined water level had a stronger response with the other two variables.

A research on a new mapping method for landslide susceptibility based on SBAS-InSAR technology

The acquisition of landslide inventory represents a pivotal challenge in landslide susceptibility mapping. Existing landslide susceptibility maps(LSMs) predominantly rely on manually obtained landslide inventories, leading to an overdependence on expert insights and susceptibilities to topographic and geomorphic influences. In regions characterized by steep terrain, obtaining a landslide inventory can be arduous or even unattainable, subsequently constraining the utility of LSMs. Addressing the limitations of conventional LSMs, this study introduces an innovative method for landslide inventory compilation and LSM creation, utilizing Small Baselines Subset Interferometry Synthetic Aperture Radar(SBAS-InSAR) technology. The study area selected for illustration is the Dongchuan district, notorious for frequent landslide occurrences. The application of SBAS-InSAR facilitated the extraction of surface deformation data, subsequently enabling the selection of landslide deformation points as samples. These samples underwent analysis through a particle swarm optimization-backpropagation neural network(PSO-BPNN) guided by deformation thresholds and the landslide developmental environment. This produced the LSM for the Dongchuan district. Subsequent validation of the LSM employed both qualitative and quantitative measures. Results elucidate that the LSM, as derived from the presented approach, primarily highlights high to very high susceptibility zones in landslide-prone areas, mirroring the spatial distribution of historical landslides. The method also achieved a commendable accuracy(ACC) of 79.59% and an area under the curve(AUC) value of 0.88. Notably, the landslide density exhibited a direct correlation with increasing susceptibility class. Such findings align with previous studies, endorsing the feasibility and reliability of the proposed approach.

The Detection and Control Factor Analysis of Active Landslides in Guizhou Province, China, Using Sentinel-1 SAR Imagery

Catastrophic landslides occur frequently in Guizhou Province, China, and the landslides in this area have special geomorphological, geological, and anthropogenic features. In order to detect and explore the distribution pattern and control factors of active landslides in Guizhou, firstly, a total of 693 active landslides throughout Guizhou Province were mapped based on the deformation rate, which was obtained by spatiotemporal filtering and Intermittent Small Baseline Subset (ISBAS) Interferometric Synthetic Aperture Radar (InSAR) techniques. Then, the relationships between the detected landslides and elevation, aspect, slope gradient, and stratigraphic lithology were analysed. Moreover, it was found that the landslides were mainly concentrated in three stratigraphic combinations, that is T1f~P2l−d, T1f~T1yn, and T2g~T1yn. Thereafter, the correlation coefficients between the landslide density and elevation and distance to the stratigraphic boundary were 0.54 and −0.19, indicating that the distribution of landslides was significantly controlled by the elevation and the boundary of specific stratigraphic combinations. Finally, we chose a typical landslide to explore how landslide development was controlled by the combined effects of elevation and stratigraphy by using ascending and descending InSAR results. We revealed that landslides occurred primarily in areas with a steep slope and a stratigraphy characterized by mudstone and sandstone.

Long-term vertical-land-motion investigation with space and terrestrial geodetic techniques near San Leon, Texas, USA

Monitoring vertical land motion (VLM) along coastlines, which influences the dynamics of sea level changes in relation to the land, is a challenging task due to its inherent high spatiotemporal variability and limited availability of observations. This study aimed to investigate the rates, patterns, and drivers of land subsidence near the coastal town of San Leon, TX, United States, since the 1990s, utilizing a range of space and terrestrial geodetic techniques. These techniques included interferometric synthetic aperture radar (InSAR), global navigation satellite systems (GNSS), tide gauge (TG), and satellite radar altimetry (SRA). The small baseline subset (SBAS) InSAR method was adopted to process 254 images from three synthetic aperture radar (SAR) sensors, i.e., ERS-2 between 1995 and 1999, ALOS-1 PALSAR between 2006 and 2011, and Sentinel-1 between 2016 and 2020. The results from InSAR subsidence maps were verified by comparing with high-accuracy vertical positioning observations at ten continuously operating GNSS (cGNSS) stations. Within the study area, a special attention was given to the Eagle Point TG station where sea level has been significantly rising relative to the sinking land. Long-term time series of land subsidence at Eagle Point obtained from sea-level difference between TG and SRA observations were confirmed and compared against InSAR and the cGNSS station observations recorded in close proximity. Gaussian Process regression (GPR) was then employed to model the VLM processes at Eagle Point using: (1) the combined results of InSAR and sea-level difference (i.e., GPR 1), and (2) the InSAR results alone (i.e., GPR 2). A 0.9 mm/yr divergence was found between GPR 1 and GPR 2 models, indicating the potential to accurately estimate long-term VLM with InSAR standalone measurements even if multi-year observation gaps intermittently occur, especially for inland areas where measurement data from other geodetic techniques, such as GNSS, TG, and SRA, are not available. Further investigations suggest that land subsidence around Eagle Point since 1998 was related to anthropogenic activities such as hydrocarbon pumping from oil and gas wells that were situated in close proximity to the TG station.

Geostatistical and multitemporal InSAR method and elastic deformation model for detailed characterizations of topographic change and magma reservoirs: Application to the Tangkuban Parahu Volcano, Indonesia

This study proposes the entire detection of the surface deformation of a volcanic area using interferometric synthetic aperture radar (InSAR) with geostatistics to specify the locations and shapes of the sources causing the deformation. We selected the Tangkuban Parahu area, West Java, Indonesia, as our study site and used a Sentinel-1 dataset. In this study, we combine persistent scatterer and small baseline subset InSAR with a geostatistical technique, i.e., turning bands simulation. This combination can compensate for undetected displacement pixels and decompose the line-of-sight displacement into vertical and horizontal east–west displacements using both the ascending and descending mode results and the least-squares method. This two-dimensional geostatistics and multitemporal InSAR approach is referred to as 2D GMT-InSAR. A detailed displacement pattern is revealed by 2D GMT-InSAR for undetected pixels in low-coherence areas. Then, the vertical displacement pattern in the study period from 2014 to 2019, mainly uplift with local subsidence, is reproduced via the joint use of the Mogi and Okada models for circular and rectangular parallelepiped sources of the deformation, respectively. Via a Bayesian inversion, one circular source, equivalent to a magma reservoir, and one dyke source are identified. The displacement pattern is likely caused by pressure increases in the magma reservoir, associated with the volcanic activation, and in the dyke, associated with the gas and fluid flows from the reservoir, which induced a phreatic explosion in July 2019. A connection of the dyke with the old Sunda volcanic system is found; this new finding is a result of the caldera collapse during the Sunda volcanic eruption.

Land subsidence studies in the Godavari Delta regions of the East coast of India using ALOS and Sentinel 1 data

Subsidence in coastal deltas is a challenging issue globally, influenced by both climate and anthropogenic pressures. Owing to its rich economic value, the Godavari Delta on the east coast of India attracts the human population and is therefore a hub of developmental activities. We quantified the line of sight (LOS) rate of land deformation in the Godavari Delta, India, for two different periods using the time series interferometric analysis of Advanced Land Observing Satellite Phase Arrayed L-band Synthetic Aperture Radar (ALOS PALSAR) (2007–2011) and C-band Sentinel-1A (2017–2020) datasets. This approach was based on the Small Baseline Subset (SBAS) algorithm, which constructed 76 (ALOS) and 333 (Sentinel-1A) pairs of interferograms. The results indicated that the majority of deltas had varying LOS deformation rates in both periods. The southern and northern coastline of the deltas showed subsidence (-10 to -20 mm/yr) and uplift (5 mm to 7 mm/yr), respectively, from 2007 to 2011. The uplift (11 mm/yr) during 2017–2020 was attributed to coastal erosion and accretion during the study period. Statistical analyses showed that the majority of the coastline experienced erosion from 2006 to 2011, ranging from 0 ‐30 m/yr. Conversely, during 2017–2019, accretion ranging from 0-30 m/yr was dominant. The Kakinada spit has recently experienced an uplift of 8 mm/yr. Many factors contributing to deformation, such as groundwater fluctuations, rainfall, land use and land cover (LULC) changes, and water quality, were explored. Groundwater level variations were strongly correlated with the rate of deformation. A direct relationship was also observed between water recharge through rainfall and the ground deformation rate. The subsidence rate was estimated as 0 to ‐8 mm/yr from 2007-2011 and -10 to -20 mm/yr for 2017–2020 in the Godavari Delta. Increased urban areas and land conversion from the LULC analysis were the causative factors of subsidence in this region. Thus, the Godavari Delta will become highly vulnerable, as the region has shown various LOS deformation rates. Studies on land subsidence are highly relevant for effective coastal management planning and management.

An Optimized Framework for Precipitable Water Vapor Mapping Using TS-InSAR and GNSS

Observations of precipitable water vapor (PWV) in the atmosphere play a crucial role in weather forecasting and global climate change research. Spaceborne Interferometric Synthetic Aperture Radar (InSAR), as a widely used modern geodetic technique, offers several advantages to the mapping of PWV, including all-weather capability, high accuracy, high resolution, and spatial continuity. In the process of PWV retrieval by using InSAR, accurately extracting the tropospheric wet delay phase and obtaining a high-precision tropospheric water vapor conversion factor are critical steps. Furthermore, the observations of InSAR are spatio-temporal differential results and the conversion of differential PWV (InSAR ΔPWV) into non-difference PWV (InSAR PWV) is a difficulty. In this study, the city of Jinan, Shandong Province, China is selected as the experimental area, and Sentinel-1A data in 2020 is used for mapping InSAR ΔPWV. The method of small baseline subset of interferometry (SBAS) is adopted in the data processing for improving the coherence of the interferograms. We extract the atmosphere phase delay from the interferograms by using SRTM-DEM and POD data. In order to evaluate the calculation of hydrostatic delay by using the ERA5 data, we compared it with the hydrostatic delay calculated by the Saastamoinen model. To obtain a more accurate water vapor conversion factor, the value of the weighted average temperature Tm was calculated by the path integral of the ERA5. In addition, GNSS PWV is used to calibrate InSAR PWV. This study demonstrates a robust consistency between InSAR PWV and GNSS PWV, with a correlation coefficient of 0.96 and a root-mean-square error (RMSE) of 1.62 mm. In conclusion, our method ensures the reliability of mapping PWV by using Sentinel-1A interferograms and GNSS observations.

Glacial Lake Outburst Flood Monitoring and Modeling through Integrating Multiple Remote Sensing Methods and HEC-RAS

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation.

Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection

Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR.

Potential sliding zone recognition method for the slow-moving landslide based on the Hurst exponent

The abrupt occurrence of the Zhongbao landslide is totally unexpected, resulting in the destruction of local infrastructure and river blockage. To review the deformation history of the Zhongbao landslide and prevent the threat of secondary disasters, the small baseline subsets (SBAS) technology is applied to process 59 synthetic aperture radar (SAR) images captured from Sentinel-1A satellite. Firstly, the time series deformation of the Zhongbao landslide along the radar line of sight (LOS) direction is calculated by SBAS technology. Then, the projection transformation is conducted to determine the slope displacement. Furthermore, the Hurst exponent of the surface deformation along the two directions is calculated to quantify the hidden deformation development trend and identify the unstable deformation areas. Given the suddenness of the Zhongbao landslide failure, the multi-temporal interferometric synthetic aperture radar (InSAR) technology is the ideal tool to obtain the surface deformation history without any monitoring equipment. The obtained deformation process indicates that the Zhongbao landslide is generally stable with slow creep deformation before failure. Moreover, the Hurst exponent distribution on the landslide surface in different time stages reveals more deformation evolution information of the Zhongbao landslide, with partially unstable areas detected before the failure. Two potential unstable areas after the Zhongbao landslide disaster are revealed by the Hurst exponent distribution and verified by the GNSS monitoring results and deformation mechanism discussion. The method combining SBAS-InSAR and Hurst exponent proposed in this study could help prevent and control secondary landslide disasters.

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

Employing a small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and hotspot analysis, this study identified 81 potential landslides in a 768.7 km2 area of Xiaojin county, eastern Tibetan Plateau. Subsequent time-series deformation analysis revealed that these potential landslides are in the secondary creep stage. The newly identified landslides were compared to a landslide inventory (LI), established through field surveying, in terms of causative factors, including altitude, slope, relief amplitude, distance to river, distance to road, and slope curvature. From the comparison, the InSAR technique showed the following advantages: (1) it identified 25 potential landslides at high altitudes (>3415 m) in addition to the low-altitude landslides identified through the field survey. (2) It obtained approximately 37.5% and 70% increases in the number of potential landslides in the slope angle ranges of 20°–30° and 30°–40°, respectively. (3) It revealed significant increases in potential landslides in every relief amplitude bin, especially in the range from 58 m to 92 m. (4) It can highlight key geological factors controlling landslides, i.e., the stratigraphic occurrence and key joints as the InSAR technique is a powerful tool for identifying landslides in all dip directions. (5) It reveals the dominant failure modes, such as sliding along the soil–rock interface and/or interfaces formed by complicated combinations of discontinuities. This work presents the significant potential of InSAR techniques in gaining deeper knowledge on landslide development in alpine forest regions.

Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

Ground deformation poses an imminent threat to urban development. This study uses the multiscale geographically weighted regression (MGWR) model to investigate the spatial heterogeneity in factors influencing ground deformation, thereby elucidating the drivers behind regional variations in ground deformation patterns. To gain insights into the characteristics of ground deformation in Zhuhai, China, and its spatial relationship with natural and anthropogenic features, we initially utilized the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method to collect data on ground deformation and its distribution across the entire area. Concurrently, remote sensing imagery was used to identify the various mechanisms affecting ground deformation during the same period, including geotectonic conditions, geographic environment, and human activities. Subsequently, we used the MGWR model to quantitatively estimate the effects of these driving force factors on ground deformation in Zhuhai. Our findings reveal significant ground deformation in specific areas, including Baijiao Town (Doumen District), Hongqi Town (Jinwan District), the Gaolan Port Economic Zone, and the northern part of Hengqin Town, with peak deformation rates reaching 117 mm/y. Key drivers of ground deformation in Zhuhai include NDVI, groundwater extraction intensity, and soft soil thickness. The application of the MGWR model, with an R-sq value of 0.910, outperformed both the global regression model ordinary least squares (OLS), with an R-sq value of 0.722, and the local regression model geographically weighted regression (GWR), with an R-sq value of 0.770, in identifying driving forces. This study can provide valuable insights for government policies aimed at mitigating the disaster risks associated with urban ground deformation.

Dynamic variations in thermal regime and surface deformation along the drainage channel for an expanding lake on the Tibetan Plateau

ABSTRACT The outburst of Zonag Lake in 2011 triggered a series of floods in the continuous permafrost region of the hinterland of the Qinghai-Tibet Plateau. This re-distributed the surface water in the basin and caused rapid expansion of the tail lake (Salt Lake). To avoid potential overflow of the expanding Salt Lake, a channel was excavated to drain the lake water into a downstream river. In this study, to investigate the permafrost thermal regime and the surface deformation around the expanding Salt Lake and the channel, in-situ monitoring sections were settled from Salt Lake to the downstream of the channel to obtain the permafrost temperature. Additionally, using small baseline subset interferometric synthetic aperture radar (SBAS-InSAR), the surface deformation around Salt Lake and the channel was measured. The data showed that the ground temperature at the channel was 0.6°C higher than the natural field and the mean subsidence rate around the channel was 1.5 mm/yr higher than that at Salt Lake. These results show that the permafrost temperature in the study area changed considerably with variations in the distance from the lake/channel, and the deformation in the study area was dominated by subsidence.

A deep learning-based interferometric synthetic aperture radar framework for abnormal displacement deformation prediction of bridges

This paper first proposes a novel framework for combining deep learning approach and Synthetic Aperture Radar (SAR) technique to evaluate and predict the condition of bridge displacement . The Long short-term memory (LSTM) neural network and the Small Baseline Subsets InSAR (SBAS-InSAR) are used to predict the longitudinal deformation of the bridge. Firstly, the proposed framework based on LSTM is established to obtain the relationship between the longitudinal deformation of the bridge and the influence parameters (such as temperature, temporal baseline, and others). In particular, the residual connection (RC) module is adapted to form the residual long short-term memory (Res-LSTM) neural network to improve prediction accuracy and robustness. A case study in Nanjing is analyzed to verify the performance of the proposed framework. The extracted datasets obtained from Sentienl-1 are divided into the training, validation, and testing dataset, which can satisfy the requirements in the engineering field. The R 2 and MSE of the testing datasets utilizing the proposed model are 93.17% and 15.62. Furthermore, the results indicated that the proposed framework based on Res-LSTM could predict and warn the abnormal structural deformation, extend the lifespan of structures, and minimize structural damage due to excessive deformation.

Deformation monitoring of long-span railway bridges based on SBAS-InSAR technology

The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property. The interferometric synthetic aperture radar (InSAR) technology has the advantage of high accuracy in bridge deformation monitoring. This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets (SBAS) InSAR technology and Sentinel-1A data. We analyzed the deformation results combined with bridge structure, temperature, and riverbed sediment scouring. The results are as follows: (1) The Ganjiang Super Bridge area is stable overall, with deformation rates ranging from −15.6 mm/yr to 10.7 mm/yr (2) The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span, which conforms to the typical deformation pattern of a cable-stayed bridge. (3) The sediment scouring from the riverbed cause the serious settlement on the bridge's east side compared with that on the west side. (4) The bridge deformation negatively correlates with temperature, with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature. The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

Monitoring the Muara Laboh Geothermal Field in Indonesia using the ISBAS Method with Sentinel-1 SAR Images

The Muara Laboh geothermal field lies in a South Solok basin zone, West Sumatra, Indonesia. Production and reinjection of geothermal fluids into the underground reservoir commonly induce crustal deformation. The study area is covered by 63.8% plantation, primary, and secondary forests, which limit the ability of conventional InSAR techniques. Therefore, an Intermittent Small BASeline Subset (ISBAS) analysis has been performed to estimate line-of sight (LOS) displacement time series due to geothermal production using the Sentinel-1 dataset between 8 March 2021 and 15 March 2022. The localized subsidence with ∼30 mm/yr rate over this tropical geothermal field has been revealed by using the ISBAS. The subsidence coincides with an area of the Muara Laboh geothermal reservoir. We suggest that geothermal production induced subsidence. In addition, the deformation in this geothermal field was controlled by faults and seasonally influenced by rainfall. Therefore, deformation variation was correlated with fluctuations in rainfall patterns. The geothermal reservoir system exhibited elastic expansion in response to seasonal recharge events during the rainy season.

Optimization of Reservoir Level Scheduling Based on InSAR-LSTM Deformation Prediction Model for Rockfill Dams

The Xiaolangdi reservoir has a storage capacity of more than 10 billion cubic meters, and the dam has significant seasonal deformation. Predicting the deformation of the dam during different periods is important for the safe operation of the dam. In this study, a long short-term memory (LSTM) model based on interferometric synthetic aperture radar (InSAR) deformation data is introduced to predict dam deformation. First, a time series deformation model of the Xiaolangdi Dam for 2017–2023 was established using Sentinel-1A data with small baseline subset InSAR (SBAS-InSAR), and a cumulative deformation accuracy of 95% was compared with the on-site measurement data at the typical point P. The correlation between reservoir level and dam deformation was found to be 0.81. Then, a model of reservoir level and dam deformation predicted by neural LSTM was established. The overall deformation error of the dam was predicted to be within 10 percent. Finally, we used the optimized reservoir level to simulate the deformation at the measured point P of the dam, which was reduced by about 36% compared to the real deformation. The results showed that the combination of InSAR and LSTM could predict dam failure and prevent potential failure risks by adjusting the reservoir levels.

Deep Learning and Machine Learning Models for Landslide Susceptibility Mapping with Remote Sensing Data

Karakoram Highway (KKH) is an international route connecting South Asia with Central Asia and China that holds socio-economic and strategic significance. However, KKH has extreme geological conditions that make it prone and vulnerable to natural disasters, primarily landslides, posing a threat to its routine activities. In this context, the study provides an updated inventory of landslides in the area with precisely measured slope deformation (Vslope), utilizing the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) and PS-InSAR (persistent scatterer interferometric synthetic aperture radar) technology. By processing Sentinel-1 data from June 2021 to June 2023, utilizing the InSAR technique, a total of 571 landslides were identified and classified based on government reports and field investigations. A total of 24 new prospective landslides were identified, and some existing landslides were redefined. This updated landslide inventory was then utilized to create a landslide susceptibility model, which investigated the link between landslide occurrences and the causal variables. Deep learning (DL) and machine learning (ML) models, including convolutional neural networks (CNN 2D), recurrent neural networks (RNNs), random forest (RF), and extreme gradient boosting (XGBoost), are employed. The inventory was split into 70% for training and 30% for testing the models, and fifteen landslide causative factors were used for the susceptibility mapping. To compare the accuracy of the models, the area under the curve (AUC) of the receiver operating characteristic (ROC) was used. The CNN 2D technique demonstrated superior performance in creating the landslide susceptibility map (LSM) for KKH. The enhanced LSM provides a prospective modeling approach for hazard prevention and serves as a conceptual reference for routine management of the KKH for risk assessment and mitigation.

Parametric Test of the Sentinel 1A Persistent Scatterer- and Small Baseline Subset-Interferogram Synthetic Aperture Radar Processing Using the Stanford Method for Persistent Scatterers for Practical Landslide Monitoring

The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal settings of the Sentinel 1A Persistent Scatterer (PS)- and Small Baseline Subset (SBAS)-InSAR using the StaMPS compared to the Global Navigation Satellite Systems (GNSS) in landslide cases. This study first revealed parameters with the suggested values, such as amplitude dispersion used to describe amplitude stability, ranging from 0.47 to 0.48 for PS and equal to or more than 0.6 for SBAS in WuWanZai, Ali Mt. The study further examined the suggested values for other factors, including the following: unwrap grid size to re-estimate the size of the grid; unwrap gold n win as the Goldstein filtering window to reduce the noise; and unwrap time win as the smoothing window (in days) for estimating phase noise distributions between neighboring pixels. Furthermore, the study substantiated the recommended settings in the Woda and Shadong landslide cases with the GNSS, inferring that the SBAS has adequate feasibility in practical landslide monitoring.