SBAS-InSAR Based Deformation Detection of Urban Land, Created from Mega-Scale Mountain Excavating and Valley Filling in the Loess Plateau: The Case Study of Yan’an City

Vegetation response to large-scale mountain excavation and city construction projects on the Loess Plateau of China

Since 2012, the "Mountain Excavation and City Construction" (MECC) project has been implemented extensively on the Loess Plateau of China, transforming gullies into flat land for urban sprawl by leveling loess hilltops to fill in valleys. However, this unprecedented human activity has caused widespread controversy over its unknown potential ecological impacts. Quantitative assessment of the impacts of the MECC project on the vegetation is key to ecological management and restoration. Taking the largest MECC project area on the Loess Plateau, Yan'an New District (YND), as the study area, this study investigated the spatiotemporal pattern of vegetation dynamics before and after the implementation of the MECC project using a multitemporal normalized difference vegetation index (NDVI) time series from 2009 to 2023 and explored the response of vegetation dynamics to the large-scale MECC project. The results showed that the vegetation dynamics in the YND exhibited significant spatial and temporal heterogeneity due to the MECC project, with the vegetation in the project-affected areas showing rapid damage followed by slow recovery. Vegetation damage occurred only in the project-affected area, and 84 % of these areas began recovery within 10 years, indicating the limited impact of the large-scale MECC project on the regional vegetation. The strong correlation between vegetation dynamics and the MECC project suggested that the destruction and recovery of vegetation in the project-affected areas was mainly under anthropogenic control, which highlights the importance of targeted ecological policies. Specifically, the MECC project induced local anthropogenic damage to the plant population structure during the land creation period, but regeneration and rational allocation of the vegetation were achieved through urbanization, gradually forming a new balanced ecological environment. These findings will contribute to a full understanding of the response of vegetation to such large-scale engineering activities and help local governments adopt projects or policies that facilitate vegetation recovery.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm-Convolutional Neural Network-Long Short-Term Memory Model

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm-convolutional neural network-long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research.

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were -90.78 mm and -83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are -35.38 mm/y and -30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning.

Monitoring and analysis of surface deformation in alpine valley areas based on multidimensional InSAR technology

Joshimath has received much attention for its massive ground subsidence at the beginning of the year. Rapid urbanization and its unique geographical location may have been one of the factors contributing to the occurrence of this geological disaster. In high mountain valley areas, the complex occurrence mechanism and diverse disaster patterns of geological hazards highlight the inadequacy of manual monitoring. To address this problem, the inversion of deformation of the Joshimath surface in multiple directions can be achieved by multidimensional InSAR techniques. Therefore, in this paper, the multidimensional SBAS-InSAR technique was used to process the lift-track Sentinel-1 data from 2020 to 2023 to obtain the two-dimensional vertical and horizontal deformation rates and time series characteristics of the Joshimath ground surface. To discover the causes of deformation and its correlation with anthropogenic activities and natural disasters by analyzing the spatial and temporal evolution of surface deformation. The results show that the area with the largest cumulative deformation is located in the northeastern part of the town, with a maximum cumulative subsidence of 271.2 mm and a cumulative horizontal movement of 336.5 mm. The spatial distribution of surface deformation is based on the lower part of the hill and develops towards the upper part of the hill, showing a trend of expansion from the bottom to the top. The temporal evolution is divided into two phases: gentle to rapid, and it is tentatively concluded that the decisive factor that caused the significant change in the rate of surface deformation and the early onset of the geological subsidence hazard was triggered by the 4.7 magnitude earthquake that struck near the town on 11 September 2021.

Multi-scale analysis of the mechanism of microbially induced calcium carbonate precipitation consolidation loess

Microbial-induced calcium carbonate precipitation (MICP) treatment of consolidated loess has the advantages of high efficiency and environmental protection. In this study, changes in the microscopic pore structure of loess before and after MICP treatment were compared and quantified, combined with test results at different scales, to better understand the mechanisms of MICP-consolidated loess. The unconfined compressive strength (UCS) of MICP-consolidated loess is significantly increased, and the stress-strain curve indicates improved strength and stability of the loess. X-ray diffraction (XRD) test results show that the signal strength of calcium carbonate crystals is significantly enhanced after loess consolidation. The microstructure of the loess was determined by scanning electron microscopy (SEM). The loess SEM microstructure images are quantitatively analyzed using comprehensive image processing methods (including gamma adjustment, grayscale threshold selection, median processing). The changes in microscopic pore area and average pore sizes (Feret diameter) of the loess before and after consolidation are described. More than 95% of the pores consist of pores with a pore area of less than 100 μm² and an average pore size of less than 20 μm. The total percentage of pore numbers with pore areas of 100-200 and 200-1000 μm² decreased by 1.15% after MICP consolidation, while those with 0-1 and 1-100 μm² increased. The percentage of pore numbers with an average pore size greater than 20 μm decreased by 0.93%, while the 0-1, 1-10, and 10-20 μm increased. Particle size distributions revealed a significant increase in particle size after MICP consolidation, with an increase of 89 μm in D₅₀.

Geodetector-Based Livability Analysis of Potential Resettlement Locations for Villages in Coal Mining Areas on the Loess Plateau of China

The resettlement of residents within the construction area of large projects is an important task related to people’s welfare. Livability is often used as an evaluation indicator when selecting resettlement areas. According to the results of the China Development Plan and 300 questionnaires, the human settlement factors that constitute livability include the living environment, ecological health, infrastructure, public facilities, and economic development, data on which can only be obtained from existing villages, and therefore cannot be used to directly assess the livability of potential resettlement areas. In fact, these human settlement factors are formed by the complex influences of numerous geographical factors (e.g., slope, slope orientation, accessibility, etc.), and it is scientific and reliable to use these geographical factors, which can be determined for each location, to carry out the livability assessment of potential resettlement areas. To this end, this paper takes the village resettlement project in the Dafosi coal mining area on the Loess Plateau of China as an example, calculates the livability scores of the existing villages around the coal mine using the entropy weighting method, and quantitatively analyzes the relationship between the livability scores and the selected geographic factors using a spatial correlations analysis method named Geodetector. It further uses the weighted overlayed function to superimpose the main geographic factors in order to obtain a livability grading map of the potential resettlement area. The results were successfully applied to the above resettlement project. We also verified the accuracy of this paper’s assessment method by adding 184 natural villages, and the method can be applied to other types of resettlement area livability assessment.

Land Subsidence Monitoring Method in Regions of Variable Radar Reflection Characteristics by Integrating PS-InSAR and SBAS-InSAR Techniques

In the InSAR solution, the uneven distribution of permanent scatterer candidates (PSCs) or slowly decoherent filtering phase (SDFP) pixel density in a region of variable radar reflection feature can cause local low accuracy in single interferometry. PSCs with higher-order coherence in Permanent Scatter InSAR (PS-InSAR) are generally distributed in those point targets of urban built-up areas, and SDFP pixels in Small Baseline Subset InSAR (SBAS-InSAR) are generally distributed in those distributed targets of countryside vegetation areas. According to the respective reliability of PS-InSAR and SBAS-InSAR for different radar reflection features, a new land subsidence monitoring method is proposed, which combines PS-SBAS InSAR by data fusion of different interferometry in different radar reflection regions. Density-based spatial clustering of applications with noise (DBSCAN) clustering analysis is carried out on the density of PSCs with higher-order coherence in PS-InSAR processing to zone the region of variable radar reflection features for acquiring the boundary of data fusion. The vector monitoring data of PS-InSAR is retained in the dense region of PSCs with higher-order coherence, and the vector monitoring data of SBAS-InSAR is used in the sparse region of PSCs with higher-order coherence. The vertical displacements from PS-InSAR and SBAS-InSAR are integrated to obtain the optimal land subsidence. The verification case of 38 SAR images acquired by the Sentinel-1A in Suzhou city indicates that the proposed method can automatically choose a matched interferometry technique according to the variability of radar reflection features in the region and improve the accuracy of using a single interferometry method. The integrated method of the combined field is more representative of overall subsidence characteristics than the PS-InSAR-only or SBAS-InSAR-only results, and it is better suited for the assessment of the impact of land subsidence over the study area. The research results of this paper can provide a useful comprehensive reference for city planning and help decrease land subsidence in Suzhou.

Displacement Back Analysis of Reservoir Landslide Based on Multi-Source Monitoring Data: A Case Study of the Cheyiping Landslide in the Lancang River Basin, China

Landslides that occur in the littoral zone of a reservoir can directly damage the hydraulic structures and threaten the lives and property around the reservoir. Due to the spatial variability and heterogeneities of rock mass, a limited amount of data obtained from laboratory and in situ tests cannot comprehensively characterize the mechanical properties of rock and soil masses. Therefore, displacement back analysis is often performed to determine the mechanical parameters of rock and soil masses. The spaceborne Interferometric synthetic aperture radar (InSAR) has proved to be a powerful tool for geodesy in the measurement of landslide movement. However, InSAR can only measure the surface motion of the landslide without the subsurface information. This study uses multi-source monitoring data in the landslide displacement back analysis, including surface InSAR and an internal borehole inclinometer. The identified material parameters and finite element simulation are used to predict the landslide deformation. The case study of the Cheyiping landslide located in the Lancang River basin demonstrates the necessity and feasibility of using multi-source monitoring data in landslide displacement back analysis. The Cheyiping landslide is currently in the creep deformation stage. The decrease in shear strength of rock masses due to the rheological deformation and the change in reservoir water level are the internal and external factors leading to excessive landslide deformation. The numerical modeling can accurately simulate the landslide movement using the identified material parameters. By combing multi-source monitoring data and numerical modeling, the reservoir landslide deformation analysis can help evaluate the landslide deformation state and stability, which is vital for reservoir risk mitigation and the sustainable development of hydropower resources.

New Insights into Ice Avalanche-Induced Debris Flows in Southeastern Tibet Using SAR Technology

Drastic climate change has led to glacier retreat in southeastern Tibet, and the increased frequency and magnitude of heavy rainfall and intense snow melting have intensified the risk of ice avalanche-induced debris flows in this region. To prevent and mitigate such hazards, it is important to derive the pre-disaster evolutionary characteristics of glacial debris flows and understand their triggering mechanisms. However, ice avalanche-induced debris flows mostly occur in remote alpine mountainous areas that are hard for humans to reach, which makes it extremely difficult to conduct continuous ground surveys and optical remote sensing monitoring. To this end, synthetic aperture radar (SAR) images were used in this study to detect and analyze the pre-disaster deformation characteristics and spatial evolution in the Sedongpu Basin and to detect changes in the snowmelt in the basin in order to improve our understanding of the triggering mechanism of the ice avalanche-induced debris flows in this region. The results revealed that the maximum average deformation rate in the basin reached 57.3 mm/year during the monitoring period from January 2016 to October 2018. The deformation displacement in the gully where the ice avalanche source area was located was intimately correlated with the summer snowmelt and rainfall and was characterized by seasonal accumulation. Clear acceleration of the deformation was detected after both the most recent earthquake and the strong rainfall and snowmelt processes in the summer of 2018. This suggests that earthquakes, snowmelt, and rainfall were significant triggers of the Sedongpu ice avalanche-induced debris flows. The results of this study provide new insights into the genesis of the Sedongpu ice avalanche-induced debris flows, which could assist in disaster warning and prevention in alpine mountain regions.

Surface Deformation of Expansive Soil at Ankang Airport, China, Revealed by InSAR Observations

Ankang Airport is constructed on an expansive soil-fill platform in Shaanxi Province, Central China. Since its completion in 2020, it has suffered surface deformation caused by the consolidation and settlement of the fill layer and instability of the expansive soil slope. Exploring the special deformation law of expansive soil regions by remote sensing and analyzing the deformation characteristics of airports in mountainous areas have always been key issues in related disaster research. Based on the intensity and phase observation data of 37 Sentinel-1 synthetic aperture radar images, this study obtained the spatio-temporal distribution of the deformation of Ankang Airport from May 2020 to October 2021. First, phase optimization was performed on the original interferograms. Second, the persistent scatterer synthetic aperture radar interferometry (PS-InSAR) method was applied to extract the surface deformation information of Ankang Airport, and the accuracy was evaluated. Finally, the singular spectrum analysis method was introduced to jointly analyze the deformation information obtained by the InSAR technology in combination with geological and climatic data. The results show that the excavation area of Ankang Airport was basically stable, the filling area had obvious surface and uneven deformation, and the expansive soil fill slope exhibits deformation characteristics strongly related to slope, rainfall, and fill depth. The deformation was mainly caused by consolidation and settlement, supplemented by the expansion and shrinkage deformation of the expansive soil.

Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest

Landslide risk assessment is important for risk management and loss–damage reduction. Herein, we assessed landslide susceptibility, hazard, and risk in the urban area of Yan’an City, which is located on the Loess Plateau of China and affected by many loess landslides. Based on 1841 slope units mapped in the study area, a random forest machine learning classifier and eight environmental factors influencing landslides were used for a landslide susceptibility assessment. In addition, differential synthetic aperture radar interferometry (DInSAR) technology was used for a hazard assessment. The accuracy of the random forest is 0.903 and the area under the receiver operating characteristics (ROC) curve is 0.96. The results show that 16% and 22% of the slope units were classified as being at very high and high-susceptibility levels for landslides, respectively, whereas 16% and 24% of the slope units were at very high and high-hazard levels for landslides, respectively. The landslide risk was obtained based on the susceptibility map and hazard map of landslides. The results show that only 26% of the slope units were located at very high and high-risk levels for landslides and these are mainly concentrated in urban centers. Such risk zones should be taken seriously and their dynamics must be monitored. Our landslide risk map is expected to provide information for planners to help them choose appropriate locations for development schemes and improve integrated geohazard mitigation in Yan’an City.

Persistent Scatterer Interferometry and Statistical Analysis of Time-Series for Landslide Monitoring: Application to Santo Stefano d’Aveto (Liguria, NW Italy)

Landslides are a major threat for population and urban areas. Persistent Scatterer Interferometry (PSI) is a powerful tool for identifying landslides and monitoring their evolution over long periods and has proven to be very useful especially in urban areas, where a sufficient number of PS can be generated. In this study, we applied PS interferometry to investigate the landslide affecting Santo Stefano d’Aveto (Liguria, NW Italy) by integrating classic interferometric techniques with cross-correlation analysis of PS time-series and with geological and geotechnical field information. We used open-source software and packages to process Synthetic Aperture Radar (SAR) images from the Copernicus Sentinel-1A satellite for both ascending and descending orbits over the period 2015–2021 and calculate both the vertical motion and the E-W horizontal displacement. By computing the cross-correlation of the PS time-series, we identified three families of PS with a similarity greater than 0.70. The cross-correlation analysis allowed subdividing the landslide in different sectors, each of which is characterized by a specific type of movement. The geological meaning of this subdivision is still a matter of discussion but it is presumably driven by the geomorphological setting of the area and by the regional tectonics.

Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

Flood Inundation Analysis in Penang Island (Malaysia) Based on InSAR Maps of Land Subsidence and Local Sea Level Scenarios

Penang Island is an important economic center in Malaysia and most of its population live in the coastal areas. Although previous studies have shown that it is vulnerable to rising sea levels, the combination of sea-level rise and local land subsidence would be devastating. Therefore, the objective of this study is to apply the local land subsidence model to estimate the inundated areas which relate to sea level rise by 2100. Land subsidence is quantified by the SBAS-InSAR technique on the basis of Sentinel-1 radar images for both ascending and descending tracks. For the first time, the geostatistical analyst method is used to merge the different track results and create the land subsidence models, the results show this method can maximize land deformation fields and minimize deformation errors. According to the land deformation results, all of the coastlines in the east of the island have differing medium levels of subsidence, especially in reclaimed lands and building areas. Lastly, the bathtub model is used to quantify the inundated areas by combing regional sea-level rise projection and local land subsidence models under CoastalDEM in 2100 projections. The results of this study indicate land subsidence that would increase 2.0% and 5.9% of the inundated area based on the different scenarios by 2100 projections.

Characterizing the Topographic Changes and Land Subsidence Associated with the Mountain Excavation and City Construction on the Chinese Loess Plateau

A mega project, Mountain Excavation and City Construction (MECC), was launched in the hilly and gully region of the Chinese Loess Plateau in 2012, in order to address the shortage of available land and create new flat land for urban construction. However, large-scale land creation and urban expansion significantly alters the local geological environment, leading to severe ground deformation. This study investigated the topographic changes, ground deformation, and their interactions due to the MECC project in the Yan’an New District (YND). First, new surface elevations were generated using ZiYuan-3 (ZY-3) stereo images acquired after the construction in order to map the local topographic changes and the fill thickness associated with the MECC project. Then, the interferometric synthetic aperture radar (InSAR) time series and 32 Sentinel-1A images were used to assess the spatial patterns of the ground deformation in the YND during the postconstruction period (2017–2018). By combining the InSAR-derived results and topographic change features, the relationship between the ground deformation and large-scale land creation was further analyzed. The results indicated that the MECC project in the YND has created over 22 km2 of flat land, including 10.8 km2 of filled area, with a maximum fill thickness of ~110 m. Significant uneven ground deformation was detected in the land-creation area, with a maximum subsidence rate of approximately 121 mm/year, which was consistent with the field survey. The strong correlation between the observed subsidence patterns and the land creation project suggested that this recorded uneven subsidence was primarily related to the spatial distribution of the filling works, along with the changes in the thickness and geotechnical properties of the filled loess; moreover, rapid urbanization, such as road construction, can accelerate the subsidence process. These findings can guide improvements in urban planning and the mitigation of geohazards in regions experiencing large-scale land construction.

A New Method to Predict Gully Head Erosion in the Loess Plateau of China Based on SBAS-InSAR

Gully head erosion causes serious land degradation in semiarid regions. The existing studies on gully head erosion are mainly based on measuring the gully volume in small-scale catchments, which is a labor-intensive and time-consuming approach. Therefore, it is necessary to explore an accurate method quantitatively over large areas and long periods. The objective of this study was to develop a model to assess gully head erosion in the Loess Plateau of China using a method based on the SBAS-InSAR technique. The gully heads were extracted from the digital elevation model and validated by field investigation and aerial images. The surface deformation was estimated with SBAS-InSAR and 22 descending ALOS PALSAR datasets from 2007 to 2011. A gully head erosion model was developed; this model can incorporate terrain factors and soil types, as well as provides erosion rate predictions consistent with the SBAS-InSAR measurements (R2 = 0.889). The results show that gully head erosion significantly depends on the slope angle above the gully head, slope length, topographic wetness index, and catchment area. The relationship between these factors and the gully head erosion rate is a power function, and the average rate of gully head erosion is 7.5 m3/m2/year, indicating the high erosional vulnerability of the area. The accuracy of the model can be further improved by considering other factors, such as the stream power factor, curvature, and slope aspect. This study indicates that the erosion rate of gully heads is almost unaffected by soil type in the research area. An advantage of this model is that the gully head area and surface deformation can be easily extracted and measured from satellite images, which is effective for assessing gully head erosion at a large scale in combination with SBAS-InSAR results and terrain attributes.

Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods

Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.

Characteristics of the Residual Surface Deformation of Multiple Abandoned Mined-Out Areas Based on a Field Investigation and SBAS-InSAR: A Case Study in Jilin, China

Residual surface deformation resulting from abandoned mined-out areas can lead to severe damage to ground structures (e.g., buildings and infrastructure in mining areas) and the local ecological environment. Long-term monitoring and analyses of surface deformation characteristics of abandoned mined-out areas are significant for preventing potential disasters. In this study, a detailed field investigation first was conducted in Ying’an and Baoshan coal mines located in Jilin Province, China, to survey mining-induced disasters in the mining areas. Based on the 40 Sentinel-1A images acquired from 14 February 2017 to 17 May 2020, small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) technology was employed to obtain the time-series residual surface deformation. Validation of the SBAS-derived results is performed by comparing with the results obtained via leveling measurements. The root mean square error (RMSE) between SBAS-derived and leveling measurements results was found to be 1.144 mm, reflecting a fairly good agreement. Furthermore, the ordinary Kriging interpolation approach was adopted to obtain information on the deformation across the entire area. The spatial–temporal evolution characteristics of the derived subsidence bowls in multiple mined-out areas were revealed. The deformation characteristics for the abandoned mined-out areas in different periods were not completely consistent. Finally, the potential mechanism underlying the inconsistency in the subsidence associated with underground coal exploitation is analyzed. The findings of this study can provide insights into local construction and ecological improvement as well as guidance for the prediction of deformation in abandoned mined-out areas.

Regional-scale monitoring of underwater and dry ground subsidence in high phreatic areas of North China Plain

Land subsidence monitoring provides information required when developing land use plans and allows for proactive management of subsidence issues. However, it has been challenging to accurately detect land subsidence areas, especially those under waterbodies. This study evaluated the applicability of integrated use of the optical Landsat-8 OLI and microwave Sentinel-1A TOPSAR imagery to delineate subsidence areas and quantify subsidence rates in a typical coal mining area of North China Plain. An Enhanced Modified Normalized Difference Water Index (E-MNDWI) was combined with Short BAseline Subset-Interferometric Synthetic Aperture Radar (SBAS-InSAR) image to monitor underwater and dry ground subsidence. The results demonstrated that the method could delineate underwater and dry ground subsidence and quantify its rates accurately. The proposed method estimated subsidence area corresponded to 34.8% (16.7 km²) of the study area. The size of underwater subsidence areas was substantial and accounted for 43.7% of the subsidence areas. Seasonal underwater subsidence areas were generally distributed in the vicinity of perennial ones. Dry ground subsidence covered 9.4 km² of the study area and generally occurred in urban and rural residential areas with the maximum subsidence of up to 80.1 mm/year. This study demonstrates the efficiency and capacity of integrating optical and microwave images to monitor the subsidence progresses, which thus can help develop effective rehabilitation policy and strategy to mitigate the impacts of land subsidence.

Deformation Time Series and Driving-Force Analysis of Glaciers in the Eastern Tienshan Mountains Using the SBAS InSAR Method

Glacier melting is one of the important causes of glacier morphology change and can provide basic parameters for calculating glacier volume change and glacier mass balance, which, in turn, is important for evaluating water resources. However, it is difficult to obtain large-scale time series of glacier changes due to the cloudy and foggy conditions which are typical of mountain areas. Gravity-measuring satellites and laser altimetry satellites can monitor changes in glacier volume over a wide area, while synthetic-aperture radar satellites can monitoring glacier morphology with a high spatial and temporal resolution. In this article, an interferometric method using a short temporal baseline and a short spatial baseline, called the Small Baseline Subsets (SBAS) Interferometric Synthetic-Aperture Radar (InSAR) method, was used to study the average rate of glacier deformation on Karlik Mountain, in the Eastern Tienshan Mountains, China, by using 19 Sentinel-1A images from November 2017 to December 2018. Thus, a time series analysis of glacier deformation was conducted. It was found that the average glacier deformation in the study region was −11.77 ± 9.73 mm/year, with the observation sites generally moving away from the satellite along the Line of Sight (LOS). Taking the ridge line as the dividing line, it was found that the melting rate of southern slopes was higher than that of northern slopes. According to the perpendicular of the mountain direction, the mountain was divided into an area in the northwest with large glaciers (Area I) and an area in the southeast with small glaciers (Area II). It was found that the melting rate in the southeast area was larger than that in the northwest area. Additionally, through the analysis of temperature and precipitation data, it was found that precipitation played a leading role in glacier deformation in the study region. Through the statistical analysis of the deformation, it was concluded that the absolute value of deformation is large at elevations below 4200 m while the absolute value of the deformation is very small at elevations above 4500 m; the direction of deformation is always away from the satellite along the LOS and the absolute value of glacier deformation decreases with increasing elevation.

Ground Deformation of the Chongming East Shoal Reclamation Area in Shanghai Based on SBAS-InSAR and Laboratory Tests

With the development of the economy, land reclamation, as a result of dredged soil, has become an effective measure to alleviate land scarcity in many coastal cities around the world. Chongming East Shoal (CES), a typical reclamation area in Shanghai that is formed by multi-phase reclamation projects, was selected as the study area. The small baseline subset–interferometry synthetic aperture radar (SBAS-InSAR) method was applied to derive the map of velocity distribution and accumulated deformation with 70 Sentinel-1 synthetic aperture radar (SAR) images collected from 22 March 2015 to 2 December 2019. In addition, 25 undisturbed soil samples, including dredger fill and underlying soil layers, were collected from five boreholes (maximum depth 55 m) through a field investigation. Laboratory tests were then performed on all soil samples in order to facilitate an understanding of geological features, including the measurement of basic physical properties, cation exchange capacity, compressibility, microscale structure, and pores. The present results show that the whole CES was undergoing differential ground deformation, with a velocity ranging from −47.5 to 34.6 mm/y. Fast (−3.4 mm/y) to slow (−0.3 mm/y) mean subsidence velocities were detected in multi-phase reclamation areas from inland areas to the coastline, and were controlled by building load and geological features of soil layers. Urbanization is the main factor that triggers accelerated subsidence and should receive special attention for reclamation areas that have been finished for a long time (over 20 years in this study). The geological features indicated that poor drainage conditions in offshore soil layers resulted in slow subsidence. The field investigation and laboratory test can be powerful explanatory tools to monitor the results from a mechanical perspective.

Characteristics of Surface Deformation in Lanzhou with Sentinel-1A TOPS

While surface deformations and their impact on buildings have been observed in the city of Lanzhou, it is difficult to find studies of surface deformation and the influential factors in the recent decades. This study was designed to detect the spatial position of these surface deformations and to understand the mechanism behind them. Sentinel-1A TOPS model image data acquired in descending orbits between March 2015 and May 2019 were processed by using Small Baseline Subsets Interferometry (SBAS)-Interferometric Synthetic Aperture Radar (InSAR) technology, and then compared with geology, tectonical aspects of the study area and land cover types in Lanzhou. The results reveal that the land surface deformation is uneven in this city, and seven surface deformation regions were detected in the following areas: the north freight yard, Jiuzhou, Country garden, Donggang, Yanjiaping, Zhongxinping and Liuquan town. The land surface deformation rate in Lanzhou ranges from −82.13 mm/year to 19.31 mm/year. Time-series land surface deformation analysis showed that deformation increased over time in major deformation regions. Surface deformation expansion was significant after June 2017and it continued to expand. The surface deformation of Lanzhou is affected by natural factors (geology and geological faults) and human activities (land cover types/land cover changes). Local geological conditions control the location of the surface deformation process. These findings provide compelling data and theoretical support for disaster prevention and reduction in Lanzhou.

Land Subsidence Related to Coal Mining in China Revealed by L-band InSAR Analysis

Geological disasters, including ground deformation, fractures and collapse, are serious problems in coal mining regions, which have threatened the sustainable development for local industry. The Ordos Basin is most known for its abundant coal resources. Over-mining the underground coal resources had induced land deformation. Detecting the evolution of the land deformation features and identifying the potential risk are important for decision-makers to prevent geological disasters. We analyzed land subsidence induced by coal mining in a 200 km2 area in the Ordos Basin for the time period 2006–2015. ALOS-1 PALSAR images from December 2006 to January 2011 and ALOS-2 PALSAR-2 images from December 2014 to July 2015, optical remotely sensed images and coal mining information were collected. The small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method and differential interferometric synthetic aperture radar (D-InSAR) method, GIS and statistical analysis were adopted. Results show that the maximum subsidence rate and cumulative subsidence along the line of sight (LOS) were −65 mm/year and −246 mm, respectively, from December 2006 to January 2011. The maximum cumulative subsidence was −226 mm from December 2014 to July 2015. The new boundary of the mining goafs from 2014 to 2015 and the most dangerous risk region were mapped. Moreover, the effect of large-scale mining coal, with the production volume exceeds 1.2 million tons per year, with the operation time more than 20 years on land subsidence was found greater than small and medium-scale coal mines and reached −59 mm/year. The recently established small-sized and medium-sized coal mines show high land subsidence. This study will contribute to better understand the land subsidence process in mining region and provide scientific support for government to prevent land subsidence.

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.