Search Results (18)

Coal mining leads to surface subsidence, landslides, soil erosion and other problems that seriously threaten the life and property safety of residents in mining areas, and it is urgent to obtain mining subsidence information using high-frequency, high-precision and large-scale monitoring methods. Therefore, this paper mainly studies the deformation monitoring of the Datong mining area using Lutan-1 monostatic and bistatic SAR data. Firstly, the latest Lutan-1 bistatic data are used to reconstruct the DSM, and the interferometric calibration method is used to improve the accuracy of the DSM. Then, the surface deformation monitoring of the mining area is implemented by using DInSAR, SBAS-InSAR and Stacking-InSAR with the reconstructed DSM data and Lutan-1 monostatic SAR data. Finally, the deformation monitoring results are compared with the surface deformation results based on the TanDEM data, and both the results are evaluated using the filed leveling data. Taking 20 images covering the Datong mining area as the data sources, the surface deformation results obtained using different InSAR methods in the mining area were quantitatively evaluated and analyzed. The results indicated that: (1) the DSM obtained using the Lutan-1 bistatic SAR data was assessed and demonstrated with the ICESat laser altimetry data an error of 2.8 m, which meets the Chinese 1:50,000 scale DEM cartographic accuracy standard, and the difference analysis with the TanDEM data shows that the terrain changes are mainly distributed in mountainous areas; (2) Due to the improvement in resolution, the registration accuracy of the SAR images and LT-DSM is higher than that of the TanDEM data in the range direction and azimuth direction; (3) Via evaluation with the filed leveling data, it is found that the surface deformation measurement results based on LT-DSM are less affected by terrain, and the accuracy of LT-DSM-SBAS and LT-DSM-DInSAR is improved by 11.5% and 16.3%, respectively, compared with TanDEM-SBAS and TanDEM-DInSAR, which demonstrates the effectiveness of the Lutan-1 bistatic and monostatic data for mine deformation monitoring. Full article

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 T_m 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. Full article

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. Full article

The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly limits the usage of the InSAR deformation map, especially for spatiotemporal characterizing and mechanism inversion. Some conventional methods (e.g., spatial interpolation) rely only on the deformation measurements without considering the influence factors, leading to the inaccuracy of the deformation prediction. So, we propose a multifactor-based machine learning model, namely the K-RFR model, that combines K-means clustering and random forest regression algorithm to reconstruct a continuous deformation map, where the influence factors on ground deformation are considered, such as land use, geological engineering, and under groundwater extraction. We take the city of Xi’an, China, as the study area where SBAS-InSAR was used to obtain the ground deformation maps from 2012 to 2015. Fourteen influence factors are employed, including confined water level, change of confined water, phreatic water level, change of phreatic water, rainfall, ground fissures, stratigraphic lithology, landform, hydrogeology, engineering geology, type of land use, soil type, GDP, and DEM, where the K-means clustering method is used to reduce the influence of spatial heterogeneity. The study area is divided into three homogeneous regions and modeled independently, where the mean squared errors of region I–III are 2.9 mm, 2.3 mm, and 3.9 mm, respectively, and the mean absolute errors are 2.5 mm, 1.0 mm, and 2.8 mm, respectively. Finally, the continuous ground deformation maps of Xi’an from 2012 to 2015 are reconstructed. We compared the new method with two interpolation methods. Results show that the correlation coefficient between prediction and InSAR measurements of the new model is 0.94, whereas the ordinary Kriging method is 0.69, and the IDW method is only 0.63. This study provides an effective means to predict the continuous surface deformation over a large area. Full article

The high-precision digital elevation model (DEM) is of great significance for improving the accuracy of InSAR deformation monitoring. In today’s free opening of multi-source DEM, there is no consensus on how to select suitable DEMs to assist InSAR in deformation monitoring for different landforms. This article introduces five types of DEMs: ALOS12.5, SRTM-1, ASTER V3, AW3D30, and Copernicus 30, and uses SBAS-InSAR technology to analyze the applicability of deformation monitoring in the Qinghai Tibet Plateau and Central China Plain regions. The coverage, average value, standard deviation, and unwrapping efficiency of the phase unwrapping results, the temporal deformation rate curves of six random deformation points in the key deformation area, as well as the consistency with the second-level data and the comparative analysis of RMSE of all deformation points, show that in the Qinghai Tibet Plateau region, Copernicus 30 is the best, followed by ASTER V3, AW3D30, and SRTM-1 having low accuracy, and ALOS12.5 is the worst. In the Central China Plain region, AW3D30 is the best, followed by Copernicus 30, SRTM-1, and ASTER V3 having low accuracy, and ALOS12.5 is still the worst. Although ALOS12.5 has the highest resolution, it is not recommended for deformation monitoring based on its worst performance in plateau and plain areas. It is recommended to use Copernicus 30 in plateau areas and AW3D30 for deformation monitoring in plain areas. Full article

It is challenging to monitor landslides due to their heavy concealment and the extreme destructiveness during the long development of landslides. Many landslide monitoring tools are somewhat onefold. In this paper, a comprehensive landslide monitoring method involving multiple factors from time-series multi-data sources is proposed. We focus on the changes in three aspects consisting of the vegetation condition, the surface deformation information and the landslide susceptibility. Firstly, the fractional vegetation cover of the landslide is extracted from optical remote sensing Gaofen-1 (GF-1) images using the dimidiate pixel model. Next, the surface deformation information of the landslide is derived from SAR remote sensing Sentinel-1A images applying the SBAS-InSAR method. Then, the landslide susceptibility based on GF-1, Sentinel-1A images and DEM data is computed using the analytic hierarchy process method. Finally, the spatio-temporal correlations of the vegetation condition, the surface deformation information and the landslide susceptibility are compared and interpreted. The Temi landslide is located along the Jinsha River and poses a high risk of blocking the river. Taking the Temi landslide as the study area, it is indicated from the results that the fractional vegetation cover, surface deformation information and landslide susceptibility reveal a consistency in the patterns of changes in spatial and temporal terms. As the surface deformation information improves, the status of the landslide vegetation also deteriorates and the landslide susceptibility becomes high, which indicates an increased probability of the creep and even the occurrence of landslides. In contrast, when the surface deformation information drops, the vegetation condition of the landslide becomes superior and the landslide becomes less susceptible, which means the likelihood of sliding declines. This study provides a new idea for a landslide monitoring method and potential way for natural disaster prevention and mitigation. Full article

In recent years, the number of geological disasters in Sichuan Province has significantly increased due to the influence of earthquakes and extreme climate, as well as the disturbance to the geological environment by human activities. Thus, geological disaster monitoring is particularly important, which can provide some scientific basis for disaster prevention and reduction. In this paper, the interferometric synthetic aperture radar (InSAR) technology was introduced to monitor potential geological hazards, taking parts of Dujiangyan City, Wenchuan County, and Mao County in Sichuan Province, China as examples. Firstly, the data such as Sentinel-1A Terrain Observation with Progressive Scans (TOPS) Synthetic Aperture Radar (SAR) images and Precision Orbit Determination (POD) precise orbit ephemerides from 2018 to 2020, high-resolution optical satellite images and Digital Elevation Model (DEM) were collected. Secondly, the Differential InSAR (D-InSAR), Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), Offset-Tracking, and Distributed Scatterer InSAR (DS-InSAR) algorithms were used to invert the surface deformation of the study area. Thirdly, according to the deformation results obtained by experiments, we used GF-1 and GF-2 optical images as a reference and combine the experimental results of InSAR algorithms to delineate the areas affected by geological disasters. A total of 49 geological disaster areas were obtained, mainly including landslides, collapses, and debris flow. Through field verification, the overall accuracy rate of InSAR deformation monitoring achieved 69.23%, and the accuracy rate of new potential hazards monitoring reached 63.64%. Among all InSAR methods, the DS-InSAR method outperformed and monitored the geological disaster areas well. Finally, the study area was divided into three elevation intervals and the applicability of different InSAR algorithms in different elevation intervals was discussed. Full article

Monitoring deformation of architectural heritage sites is important for the quantitative evaluation of their stability. However, deformation monitoring of sites in mountainous areas remains challenging whether utilizing global navigation satellite system (GNSS) or interferometric synthetic aperture radar (InSAR) techniques. In this study, we improved the small baseline subset (SBAS) approach by introducing the pseudo-baseline combination strategy to avoid the errors caused by inaccurate external DEM, resulting in robust deformation estimations in mountainous areas where the architectural heritage site of the Great Wall is located. First, a simulated dataset and a real dataset were used to verify the reliability and effectiveness of the algorithm, respectively. Subsequently, the algorithm was applied in the landscape deformation monitoring of the Shanhaiguan section of the Great Wall using 51 Sentinel-1 scenes acquired from 2016 to 2018. A thematic stability map of the Shanhaiguan Great Wall corridor was generated, revealing that the landscape was generally stable save for local instabilities due to to unstable rocks and wall monuments. This study demonstrated the capabilities of adaptive multitemporal InSAR (MTInSAR) approaches in the preventive landscape deformation monitoring of large-scale architectural heritage sites in complex terrain. Full article

The area of Taiwan is 70% hillsides. In addition, the topography fluctuates wildly, and it is active in earthquakes and young orogenic movements. Landslides are a widespread disaster in Taiwan. However, landslides are not a disaster until someone enters the mountain area for development. Therefore, landslide displacement monitoring is the primary task of this study. Potential landslide areas with mostly slate geological conditions were selected as candidate sites in this study. The slate bedding in this area is approximately 30 to 75 degrees toward the southeast, which means that creep may occur due to gravity deformation caused by high-angle rock formation strikes. In addition, because the research site is located in a densely vegetated area, the data noise is very high, and it is not easy to obtain good results. This study chose ESA Sentinel-1 data for analysis and 1-m LiDAR DEM as reference elevation. The 1-m LiDAR DEM with high accuracy can help to detect more complex deformation from DInSAR. The Sentinel-1 series of satellites have a regular revisit period. In addition, the farm areas of roads, bridges, and buildings in the study area provided enough reflections to produce good coherence. Sentinel-1 images from March 2017 to June 2021 were analyzed, obtaining slope deformation and converting it to the vertical direction. Deformation derived from SAR is compared with other measurements, including GNSS and underground slope inclinometer. The SBAS solution process provides more DInSAR pairs to overcome the problem of tremendous noise and has increased accuracy. Moreover, the SBAS method’s parameter modification derives more candidate points in the vegetated area. The vertical deformation comparison between the GNSS installation location and the ascending SBAS solution’s vertical deformation is consistent. Moreover, the reliable facing of the slope toward the SAR satellite is discussed. Due to the limitations of the GNSS stations, this study proposes a method to convert the observed deformation from the slope inclinometer and convert it to vertical deformation. The displacement of the slope indicator is originally a horizontal displacement. It is assumed that it is fixed at the farthest underground, and the bottom-to-top movement is integrated with depth. The results show that the proposed equation to convert horizontal to vertical displacement fits well in this condition. The activity of landslides within the LiDAR digital elevation model identified as scars is also mapped. Full article

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. Full article

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series. Full article

Recently, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique is widely used for quantifying the land surface deformation, which is very important to assess the potential impact on social and economic activities. Radar satellites operate in different wavelengths and each provides different levels of vertical displacement accuracy. In this study, the accuracies of Sentinel-1 (C-band) and ALOS/PALSAR-2 (L-band) were investigated in terms of estimating the land subsidence rate along the study area of Alexandria City, Egypt. A total of nine Sentinel-1 and 11 ALOS/PALSAR-2 scenes were used for such assessment. The small baseline subset (SBAS) processing scheme, which detects the land deformation with a high spatial and temporal coverage, was performed. The results show that the threshold coherence values of the generated interferograms from ALOS-2 data are highly concentrated between 0.2 and 0.3, while a higher threshold value of 0.4 shows no coherent pixels for about 80% of Alexandria’s urban area. However, the coherence values of Sentinel-1 interferograms ranged between 0.3 and 1, with most of the urban area in Alexandria showing coherent pixels at a 0.4 value. In addition, both data types produced different residual topography values of almost 0 m with a standard deviation of 13.5 m for Sentinel-1 and −20.5 m with a standard deviation of 33.24 m for ALOS-2 using the same digital elevation model (DEM) and wavelet number. Consequently, the final deformation was estimated using high coherent pixels with a threshold of 0.4 for Sentinel-1, which is comparable to a threshold of about 0.8 when using ALOS-2 data. The cumulative vertical displacement along the study area from 2017 to 2020 reached −60 mm with an average of −12.5 mm and mean displacement rate of −1.73 mm/year. Accordingly, the Alexandrian coastal plain and city center are found to be relatively stable, with land subsidence rates ranging from 0 to −5 mm/year. The maximum subsidence rate reached −20 mm/year and was found along the boundary of Mariout Lakes and former Abu Qir Lagoon. Finally, the affected buildings recorded during the field survey were plotted on the final land subsidence maps and show high consistency with the DInSAR results. For future developmental urban plans in Alexandria City, it is recommended to expand towards the western desert fringes instead of the south where the present-day ground lies on top of the former wetland areas. Full article

Differential Interferometric SAR (DInSAR) has been used to monitor surface deformations in open pit mines and tailings dams. In this paper, ground deformations have been detected on the area of tailings Dam-I at the Córrego do Feijão Mine (Brumadinho, Brazil) before its catastrophic failure occurred on 25 January 2019. Two techniques optimized for different scattering models, SBAS (Small BAseline Subset) and PSI (Persistent Scatterer Interferometry), were used to perform the analysis based on 26 Sentinel-1B images in Interferometric Wide Swath (IW) mode, which were acquired on descending orbits from 03 March 2018 to 22 January 2019. A WorldDEM Digital Surface Model (DSM) product was used to remove the topographic phase component. The results provided by both techniques showed a synoptic and informative view of the deformation process affecting the study area, with the detection of persistent trends of deformation on the crest, middle, and bottom sectors of the dam face until its collapse, as well as the settlements on the tailings. It is worth noting the detection of an acceleration in the displacement time-series for a short period near the failure. The maximum accumulated displacements detected along the downstream slope face were −39 mm (SBAS) and −48 mm (PSI). It is reasonable to consider that Sentinel-1 would provide decision makers with complementary motion information to the in situ monitoring system for risk assessment and for a better understanding of the ongoing instability phenomena affecting the tailings dam. Full article

Coastal dams along the Yellow River Delta are built to prevent seawater intrusion. However, land subsidence caused by significant oil, gas and brine extraction, as well as sediment compaction, could exacerbate the flooding effects of sea-level rise and storm surge. In order to evaluate the coastal dam vulnerability, we combined unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) with small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) results to generate an accurate coastal dam digital elevation model (DEM) over the next 10, 30 and 80 years. Sea-level simulation was derived from the relative sea-level rise scenarios published by the Intergovernmental Panel on Climate Change (IPCC) and local long-term tide gauge records. Assuming that the current rate of dam vertical deformation and sea-level rise are linear, we then generated different inundation scenarios by the superposition of DEMs and sea-levels at different periods by way of a bathtub model. We found that the overtopping event would likely occur around Year 2050, and the northern part of the dam would lose its protective capability almost entirely by the end of this century. This article provides an alternative cost-effective method for the detection, extraction and monitoring of coastal artificial infrastructure. Full article

The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise. Full article