Differential Interferometric Synthetic Aperture Radar Research Articles

An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

Detection of Active Landslides in Southwest China using Sentinel-1 and ALOS-2 Data

Abstract Synthetic aperture radar interferometry (InSAR) is the technique capable of monitoring active landslide in all-weather conditions and in a near real-time. In this study, ALOS-2 (L-band) and Sentinel-1 (C-Band) images were used to map potential areas prone to the occurrence of large-scale landslide disasters. Differential InSAR (D-InSAR) and multi-temporal techniques were applied to detect landslides in a part of Maoxian(a typical mountainous county in Southwest China), allowing the identification of 8 active landslides. The integration of local historical geological hazard data and local field exploration data allowed to conclude that the detected landslides found in Baibu village and other places were in the development stage. Deformation rate reached 200mm/yr along the line of sight direction. The results obtained by exploiting the two datasets correspond to each other in terms of spatial distribution and are consistent with the local field exploration results. L-band ALOS-2 SAR data proved to be very effective for detecting short-term and severe ground deformation, which is suitable for monitoring deformation in mountainous areas with a certain degree of vegetation coverage.

Automatic Detection of Volcanic Unrest Using Blind Source Separation With a Minimum Spanning Tree Based Stability Analysis

Repeated synthetic aperture radar (SAR) acquisitions can be utilized to produce measurements of ground deformations and associated geohazards, such as it can be used to detect signs of volcanic unrest. Existing time series algorithms like permanent scatterer analysis and small baseline subset are computationally demanding and cannot be applied in near real time to detect subtle, transient, and precursory deformations. To overcome this problem, we have adapted a minimum spanning tree based spatial independent component analysis method to automatically detect sources related to volcanic unrest from a time series of differential interferograms. For a synthetic dataset, we first utilize the algorithm's capability to isolate signals of geophysical interest from atmospheric artifacts, topography, and other noise signals, before monitoring the evolution of these signals through time in order to detect the onset of a period of volcanic unrest, in near real time. In this article, we first demonstrate our approach on synthetic datasets having different signal strengths and temporal complexities. Second, we demonstrate our approach on a couple of real datasets, one acquired in 2017–2019 over the Colima volcano, Mexico, showing the occurrence of previously unrecognized short-term deformation events and the other over Mt. Thorbjorn in Iceland acquired over 2020. This shows the strength of the deep learning application to differential interferometric SAR measurements, and highlights that deformation events occurring without eruptions, which may have previously been undetected.

Utilization of Sentinel-1 satellite imagery data to support land subsidence analysis in DKI Jakarta, Indonesia

Land subsidence had been a significant problem in DKI Jakarta and Semarang, with at least 20 kilometres of roads affected. Repairing them will require at least US $ 1 million per kilometre. Land subsidence monitoring has been carried out using terrestrial methods (GPS and levelling), which are believed to have a high degree of accuracy. The high accuracy of the terrestrial method results in a lack of precision over a large area. On the other hand, remote sensing technology as a non-terrestrial method has developed to monitor land subsidence which can produce high precision over a large area. This study aimed to test the Sentinel-1 satellite data using the Differential Interferometric Synthetic Aperture Radar (DInSAR) method in monitoring land subsidence in DKI Jakarta. DInSAR is a method in Remote Sensing that utilizes radar sensors to analyze the phase differences of a SAR data pair that have different times of capture and have been catalogued to obtain displacement along the area of collection. The results showed that the North Jakarta area experienced the highest land subsidence in the entire Jakarta area. The annual average rate from 2017-2019 is 3.4 cm. The value of 3.4 cm is the average value of all samples in the North Jakarta area. The second area where high land subsidence is West Jakarta, where the maximum amount value of subsidence is 2.8 cm. The accuracy-test results with the MONAS test point showed that the difference between field data and DInSAR results was ± 6.5 cm. The results of this research indicate that the DInSAR method is quite capable of describing land subsidence in the DKI Jakarta area with a relatively good level of precision.

Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico

To address increasing water demands in expanding cities, many aquifers in Mexico are overexploited and deplete. The resulting land subsidence often combines with ground faulting/fracturing and damage to infrastructure. This study provides the longest Synthetic Aperture Radar (SAR) survey ever undertaken for the Aguascalientes Valley, aimed to constrain its structurally-controlled subsidence process and the induced risk. 275 ERS-1/2 1996–2002, ENVISAT 2003–2010 and Sentinel-1 2014–2020 C-band SAR images are processed with change detection, differential Interferometric SAR (InSAR) and Small Baseline Subset (SBAS) methods. Aguascalientes notably expanded over the last four decades, as revealed by Seasat 1978 L-band SAR, Landsat 1985–2010 and Sentinel-2 2020 optical imagery. The observed subsidence pattern involves alluvial/fluvial deposits within the N-S trending graben. Maximum settlement rates are −14 cm/year in 1996, −10 cm/year in 2000–2010 and over −12 cm/year in 2015–2020. An acceleration (−0.70 cm/year2) is recorded in 2015–2020 close to recently developed industrial plants and housing districts. Satellite estimates agree with in-situ observations, static GPS surveying and continuous GPS monitoring data. Rough correlation is found with piezometric level drop rates, whereas aquifer thickness plays a stronger role in the subsidence process. While these outcomes align with the existing literature, this InSAR survey: (i) unveils previously unknown E-W deformation affecting two N-S oriented bands within the valley, with up to ~ ±3 cm/year in 2015–2020 towards its center; (ii) identifies zones of sagging and hogging with horizontal strain (ε) of up to 0.05–0.1%; (iii) retrieves differential rates reaching 6–8 cm/year and angular distortions (β) of 1/500 along the Oriente fault; and (iv) investigates the statistical distribution of β across field surveyed faults and fissures, and marks areas with potentially yet-unmapped ground discontinuities. A new surface faulting risk matrix embedding β and ε is therefore proposed to estimate subsidence impact on properties and population. Given its scale-dependency, the risk assessment provides a lower bound to the percentage of urban areas at risk within the Aguascalientes state: at least 2% of the urban areas were at high and very high risk in 2003–2010 (involving ~12,000 properties and ~39,000 inhabitants), but this increased to 6% in 2015–2020 (~25,600 properties, ~85,200 inhabitants). The evidence of a subsidence process evolving spatially and temporally highlights the need for continuous updating of hazard information.

Quantifying relationships between subsidence and longwall face advance using DInSAR

Surface subsidence that results from longwall mining can be large magnitude and can affect significant areas. Conventional methods for subsidence monitoring include leveling, global positioning system (GPS), and photogrammetric surveys. Remote sensing techniques including, aerial LiDAR, terrestrial laser scanning, and satellite-based Differential Interferometric Synthetic Aperture Radar (DInSAR), are also used to measure deformation associated with subsidence. DInSAR data are different than data from conventional subsidence surveys. Images capture data over large areas (hundreds of kilometers), and each pixel (data point) in an image quantifies the average displacement over an area of square meters. DInSAR data can have fairly high time resolution; imaging periods typically range from weeks to months. DInSAR data can be useful to monitor subsidence sequentially over short periods. Regularly monitoring subsidence may help define if caving is progressing normally and can establish relationships between surface deformation and longwall face advance, which has potential to help quantify possible risks to mine stability. In this study, subsidence at a longwall trona mine is monitored over short periods, typically 12 days, as the longwall face is advanced through a panel. C-band interferometric wide swath synthetic aperture radar (SAR) images from the sentinel satellites are used to quantify the subsidence. The onset of subsidence occurs close in time to the beginning of the longwall face advance, and overall, the development of subsidence closely follows the longwall face advance.

Detection and Monitoring of Post-Earthquake Landslides in Jiuzhaigou Using Radar Remote Sensing

Objectives On 8th August 2017, a catastrophic earthquake of Ms 7.0 struck the County of Jiuzhaigou in Sichuan Province, China. Because of its high magnitude and shallow epicenter, the earthquake caused grave casualties and property losses. Furthermore, the earthquake triggered numerous secondary mountain disasters such as rockfall and landslide. We use the synthetic aperture radar interferometry (InSAR) technique to detect post-earthquake landslides in Jiuzhaigou area. Methods Firstly, we carry out a quick detection across wide area using differential InSAR (DInSAR) technique with 6 ALOS-2 PALSAR-2 ascending images. DInSAR technique can observe the subtle deformation of landslide surface during the period between two SAR observations, so the active deforming landslides can be efficiently distinguished from stable areas. Nevertheless, the performance of DInSAR is often limited by temporal and spatial decorrelations induced by dense vegetation coverage, etc. To retrieve the temporal evolution of these landslides, detailed monitoring of specific landslides is carried out using time-series InSAR technique with 112 Sentinel-1 ascending and descending images. Time-series InSAR technique can overcome the limitation of traditional DInSAR technique in spatial-temporal decorrelation, and improve the deformation measurement accuracy. Therefore, the combination of DInSAR and time-series InSAR is adopted to accurately monitor single landslide, and efficiently detect landslides in Jiuzhaigou area. Results The results show that there are 13 landslides that can be detected by InSAR analyses in Jiuzhaigou area, including 7 landslides close to residential areas. The time series displacements retrieved indicate that several landslides have long-term linear deformations after the 2017 earthquake. These landslides severely threaten people's life and property and need to be monitored regularly. Finally, the reliability of detection results based on InSAR technology is verified by a field survey. Conclusions The results can provide key supports for the geohazard mitigation and prevention in the Jiuzhaigou scenic area. We indicate that a strategy which combines DInSAR technique with time-series InSAR can efficiently and accurately detect and monitor landslides over a wide area.

Predicting mining-induced dynamic deformations for drilling solution rock salt mine based on probability integral method and weibull temporal function

ABSTRACT Long-term dynamic monitoring and sustainable prediction of rock salt mines is extremely important in preventing potential geological damages. However, the future prediction of the mining-induced surface displacement after the underground mining extraction cannot be achieved with single Multi-Temporal Differential Interferometric Synthetic Aperture Radar (MT-InSAR) technique, which can only reveal the deformation history during the period of Synthetic Aperture Radar (SAR) acquisition dates. In this paper, a novel dynamic model, namely Dynamic Probability Integral Method (DPIM), based on the integration of Probability Integral Method (PIM) and Weibull temporal function, is proposed in order to predict the future dynamic subsidence for the period which can not covered by the SAR acquisitions. Firstly, the time series deformation over the test salt mine are generated by Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with use of 43 Sentinel-1 images, and then the functional relationship between the derived deformation and DPIM is established; Subsequently, the unknown parameters, including the PIM parameters and the Weibull temporal parameters are estimated following the genetic algorithm; Finally, the dynamic surface deformation occurring during the entire period of the underground extraction of the test rock salt mine is predicted. Both simulation and real deformation data over Xinhua (a typical rock salt mine in Hunan province, China) have been investigated. The scenario using the simulated data shows that the overall root-mean-square error of DPIM-predicted subsidence is ±3 mm. The real data experimental results show that the maximum predicted vertical subsidence over the rock salt mine was up to 246 mm. The root-mean-square error with comparison to the levelling measurements is estimated as ±7 mm.

INTEGRATION OF GIS AND ADVANCED REMOTE SENSING TECHNIQUES FOR LANDSLIDE HAZARD ASSESSMENT: A CASE STUDY OF NORTHWEST SYRIA

Abstract. Landslide susceptibility and hazard mapping has been developed providing remarkable results through the integration of geographic information system (GIS) and remote sensing. In this regard, some approaches have considered the use of Sentinel-1 data and time-series interferometric synthetic aperture radar (InSAR) techniques, such as differential InSAR (D-InSAR) and persistent scatterers interferometric (PSI), for providing precise information about total amount and velocity of ground-surface deformations and landslides within a specific area during a specific time period which is important for disaster management’s planning process.In this paper, artificial neural network (ANN) was used as a statistical analysis method for landslide susceptibility mapping in Northwest Syria using multi-layer perceptron (MLP) neural network on a training dataset of one dependent variable (landslide or non-landslide) and nine independent variables (slope, aspect, curvature, land cover, NDVI, lithology, distance from faults, distance from road, distance from stream networks). The resulting map of landslide susceptibility was validated using area under curve (AUC) analysis using a testing dataset which showed 90.28% of AUC value. Then, landslide susceptibility map was reclassified into high-moderate-low classes and integrated with intensity map of mean velocity of ground-surface deformations during the time period form (16 October 2018) until (21 March 2019) by using a landslide hazard matrix in a GIS environment in order to get landslide hazard map of the study area for that time period. The result shows that around 44.4%, 52.9% and 2.5% of total study area was classified as a high, moderate and low hazard zone of landslide, respectively.

Geodetic Source Modeling of the 2019 Mw 6.3 Durrës, Albania, Earthquake: Partial Rupture of a Blind Reverse Fault

AbstractWe address geometric and kinematic properties of the Mw 6.3 26 November 2019 Durrës earthquake, the strongest earthquake in Albania in the past 40 years. Using coseismic surface displacements from Sentinel‐1 Differential Interferometric Synthetic Aperture Radar (DInSAR) and nearby Global Navigational Satellite System (GNSS) stations, we invert for the geometry and slip of the causative fault. We find that both a steep SW‐dipping fault (dip 71°) and a shallow NE‐dipping fault (dip 15°) can fit the data equally well. However, the slip on the SW‐dipping fault occurs at depths 11–23 km, similar to the depths of the mainshock and aftershock seismicity, and thus, we prefer that model. The location of our preferred fault plane correlates with the mapped SW‐dipping backthrust, the Vore fault. The fault rupture did not reach the surface, which implies that an updip stress propagation onto the unruptured shallow portion of the Vore fault and its secondary structures pose an increased seismic hazard for cities in Albania, including the capital, Tirana.

Deformations Prior to the Brumadinho Dam Collapse Revealed by Sentinel-1 InSAR Data Using SBAS and PSI Techniques

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.

Monitoring surface subsidence in the Binchang mining area using small baseline subset differential interferometric synthetic aperture radar with Sentinel-1A data

Surface subsidence caused by mining is difficult to monitor. Traditional field measurements are time-consuming and labor intensive. Small baseline subset (SBAS) technique is a long-term sequence method based on the principles of radar interferometry that can be used to effectively survey small ground surface deformations. This technique has been successfully applied to urban subsidence in previous studies, but it has rarely been used to study subsidence in mining areas. A total of 62 Sentinel-1A images taken from March 2017 to March 2019 were used to analyze the surface subsidence of the Binchang mining area in Shaanxi Province, China. The accuracy was evaluated using the measured field data from six ground control points for eight different months. The root-mean-square-error and correlation coefficient (r) for the data obtained using SBAS and field measurements were 2.27 cm and 0.944, respectively. We examined 12 main subsidence areas with a mean subsidence rate of −11.88 cm / year, where the maximum cumulative subsidence of a single deformation point reached 83.46 cm. The subsidence areas also had differences in spatial distribution and subsidence formation processes. The geological and groundwater conditions were the same for all areas of the study, and mining in the surrounding coal mines was the main cause of surface subsidence. We demonstrate that SBAS differential interferometric synthetic aperture radar technique can be used as an effective method for the long-term monitoring of mining deformation.

Long-Term Remote Monitoring of Ground Deformation Using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR): Applications and Insights into Geotechnical Engineering Practices

Development of synthetic aperture radar (SAR) technology and the dedicated suite of processing tools have aided the evolution of remote sensing techniques for various Earth Observation (EO) applications. Interferometric SAR (InSAR) is a relatively new geodetic technique which provides high-speed and reliable geographic, geologic, and hazards information allowing the prognosis of future environmental and urban planning. In this study, we explored the applicability of two differential interferometry techniques, conventional and advanced differential InSAR (A-DInSAR), for topographic mapping and long-term geotechnical monitoring by exploiting satellite data, particularly Sentinel-1 SAR data, which is publicly shared. We specifically used the open-source tools of SeNtinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) for interferometric data processing to implement A-DInSAR. This study presents various applications, which include generation of a digital elevation model (DEM), mapping of seismically induced displacement and associated damages, and detection and long-term monitoring of tunneling-induced ground deformation and rainfall-induced landslide. Geometric and temporal decorrelations posed challenges and limitations in the successful implementation of Sentinel-1 SAR interferometry specifically in vegetated areas. The presented results proved the validity and reliability of the exploited SAR data and InSAR techniques for addressing geotechnical engineering related problems.

Estimation of Deformation Intensity above a Flooded Potash Mine Near Berezniki (Perm Krai, Russia) with SAR Interferometry

In this study we used RADARSAT-2 and Sentinel-1 Synthetic Aperture Radar data for measuring subsidence above a flooded potash mine, which is almost entirely located within the city of Berezniki (Perm Krai, Russia), population 150,000. This area has experienced very fast subsidence since October 2006 when the integrity of the Berezniki-1 mine was compromised, resulting in water intrusion, subsequent flooding and closure of the mine. Due to the ongoing dissolution of carnallite, subsidence in this region is expected to continue in the foreseeable future. In addition to rapid subsidence, at least five sinkholes have formed in the region, with the largest being 440 × 320 m. We observed ground subsidence during the period October 2011–April 2014 (RADARSAT-2) with a vertical rate up to 14 cm/year and horizontal rate up to 10 cm/year; during the period July 2016–June 2020 (Sentinel-1) with a vertical rate up to 17 cm/year. Our results were validated by precise leveling, with a coefficient of correlation of 0.75. Subsidence faster than 17 cm/year observed by precise leveling was not resolvable with Differential Interferometric Synthetic Aperture Radar (DInSAR). Our results show the complementary nature of ground-based and space-borne measurement techniques. The precise leveling captures subsidence along profile lines with high precision but lower temporal resolution, while DInSAR captures subsidence with high spatial and temporal resolutions but with lower precision. DInSAR is also significantly affected by decorrelation outside of urban areas. An important advantage of our methodology is the ability to measure the horizontal east component of the ground deformation when both, ascending and descending, data are available. This measurement directly characterizes the level of anthropogenic load on buildings and infrastructure. We recommend continuing monitoring subsidence using both measurement techniques, which can also be complemented by continuous Global Navigation Satellite System (GNSS).

Sinkholes threatening places of worship in the historic center of Naples

The historic center of Naples (southern Italy) is characterized by a variety of elements which, forming its cultural asset, have contributed to its recognition as a UNESCO World Heritage site. Among these, cathedrals, churches and chapels are particularly relevant due to their diffusion and differentiation in age. A singular feature of these places of worship is the presence of a frequently connected network of underground cavities. Such cavities, excavated for quarrying tuff blocks, are susceptible to sudden collapses generating sinkholes which in a number of cases have been responsible for the loss of human lives. On this basis and considering the importance of such elements as attractors of visitors, an analysis of susceptibility to sinkholes of cathedrals, churches and chapels has been here carried out. A sinkhole, cavities and places of worship inventory was prepared using available data and was used, in association to ongoing ground deformation data obtained by DInSAR. Results indicated that 40% of the inventoried sinkholes are a product of cavity collapses and in 45 cases cavities lie underneath places of worship. Geodetic data obtained by Differential Interferometric SAR technique indicate that approximately 1 km2 of the historic center of Naples is subject to ongoing subsiding deformation that in several cases is localized above cavities. The susceptibility map derived from available data indicate the presence of a zone where no cathedral, church and chapel can be subject to sinkholes (1) and 3 zones where they can be subject to sagging (2), potential future cavity collapses (3) and future cavity collapses anticipated by ongoing deformation (4); in detail, 34 places of worship are in zone 2, 57 in zone 3 and 9 in zone 4. Considering an increasing susceptibility level of the identified zones, the 9 places of worship located in zone 4 require a quick response in terms of characterization, stability analysis and real time monitoring have been identified.

First assessment of the interferometric capabilities of SAOCOM-1A: New results over the Domuyo Volcano, Neuquén Argentina

Differential Interferometric Synthetic Aperture Radar (DInSAR) has been used for measuring ground deformations with high both spatial and temporal resolutions. The effectiveness of this technique has been extensively proved using mainly C and X band because of the availability of SAR platforms operating in these frequencies. In vegetated areas, L-band SAR is more adequate because of their less sensitivity to temporal decorrelation. This work presents a review of the characteristics of the L-band Argentinian satellite SAOCOM-1A and its potential in deformation monitoring. In order to show it, we processed a dataset acquired over the Domuyo Volcano (Neuquén, Argentina). Time series and mean deformation maps are validated against those computed using a Sentinel-1 dataset spanning the same time period. Results show an inflation pattern of 6 cm between August 2019 and May 2020. As expected and despising the scarce availability of SAOCOM scenes, its mean velocity map is admittedly more coherent in comparison with the Sentinel-1. Thus, we demonstrate, for the first time, the computation of deformation time series using SAOCOM data.

Landslide Monitoring and the Inventory Map Validation by Ensemble DInSAR Processing of ASAR and PALSAR Images (Case Study: Doab-Samsami Basin in Chaharmahal and Bakhtiari Province, Iran)

Applications of Synthetic Aperture Sensors (SAR) and particularly Differential Interferometric Synthetic Aperture Radar (DInSAR) have provided new opportunities for detecting and monitoring of slow and even fast land deformations such as landslides and also updating their inventory maps. Employing this technique has made possible continuous detection and monitoring of small land movements with high precision over a wide spread area. In this study, two image series including 12 radar images with descending orbit acquired by ASAR sensor of ENVISAT satellite and 10 radar images with ascending orbit collected by PALAR sensor of ALOS satellite were selected and processed by the DInSAR method in order to detect landslides in the Doab-Samsami basin in Chaharmahal and Bakhtiari province, Iran. Landslides detected in the study area cover over 5959 hec according to the processing of ASAR and PALSAR images collected between 2003 and 2011, whereas landslides detected by field studies cover over 5056 hec. Based on the results of the radar processing technique for detecting and mapping of landslides, the ASAR images can provide more details of slides due to their shorter wavelengths but the PALSAR images have comparatively greater penetration and lower incoherence due to the longer wavelengths. Results of the receiver operator characteristic (ROC) method show a well agreement between the landslides map provided by the DInSAR approach and field study. The area under the curve of receiver operator characteristic (ROC) curve was estimated to be 0.95 with a standard deviation of 0.02 at 95% confidence level. The Cohen’s Kappa of 0.61 indicate relatively good conformity between classification of the detected landslide distribution in the study area based on the DInSAR method and field survey.

Spatiotemporal behavior of a large-scale landslide at Mt. Onnebetsu-dake, Japan, detected by three L-band SAR satellites

We applied differential InSAR analysis to the Shiretoko Peninsula, northeastern Hokkaido, Japan. All the interferograms of long temporal baseline (~ 3 years) processed from SAR data of three L-band satellites (JERS-1, ALOS, ALOS-2) commonly indicate remarkable phase changes due to the landslide movement at the southeastern flank of Mt. Onnebetsu-dake, a Quaternary stratovolcano. The area of interferometric phase change matches to known landslide morphologies. Judging from the timing of the SAR image acquisitions, this landslide has been moving at least from 1993 to the present. Successive interferograms of 1-year temporal baseline indicate the temporal fluctuation of the landslide velocity. Especially for the descending interferograms, the positive line-of-sight (LOS) length change, which indicates large subsidence relative to the horizontal movement, is observed in the upslope section of the landslide during 1993–1998, while the negative LOS change is observed in the middle and the downslope section after 2007 indicating less subsidence. The landslide activity culminates from 2014 to 2017: the eastward and the vertical displacement rates reach ~ 6 and ~ 2 cm/yr, respectively. Utilizing high spatial resolution of ALOS and ALOS-2 data, we investigated velocity distribution inside the landslide. During 2007–2010, the eastward component of surface displacement increases toward the east, implying that the landslide extends toward the east. During 2014–2017, the vertical displacement profile exhibits spatially periodic uplift and subsidence consistent with surface gradient, which indicates the ongoing deformation driven by gravitational force. Heavy rainfall associated with three typhoons in August 2016 might have brought about an increase in the landslide velocity, possibly due to elevated pore-fluid pressure within and/or at the base of the landslide material. Also, annual rainfall would be an important factor that prescribes the landslide velocity averaged over 3 years.

Estimation of Snow Depth in the Hindu Kush Himalayas of Afghanistan during Peak Winter and Early Melt Season

The Pamir ranges of the Hindu Kush regions in Afghanistan play a substantial role in regulating the water resources for the Middle Eastern countries. Particularly, the snowmelt runoff in the Khanabad watershed is one of the critical drivers for the Amu River, since it is a primary source of available water in several Middle Eastern countries in the off monsoon season. The purpose of this study is to devise strategies based on active microwave remote sensing for the monitoring of snow depth during the winter and the melt season. For the estimation of snow depth, we utilized a multi-temporal C-band (5.405 GHz) Sentinel-1 dual polarimetric synthetic aperture radar (SAR) with a differential interferometric SAR (DInSAR)-based framework. In the proposed approach, the estimated snowpack displacements in the vertical transmit-vertical receive (VV) and vertical transmit-horizonal receive (VH) channels were improved by incorporating modeled information of snow permittivity, and the scale was enhanced by utilizing snow depth information from the available ground stations. Two seasonal datasets were considered for the experiments corresponding to peak winter season (February 2019) and early melt season (March 2019). The results were validated with the available nearest field measurements. A good correlation determined by the coefficient of determination of 0.82 and 0.57, with root mean square errors of 2.33 and 1.44 m, for the peak winter and the early melt season, respectively, was observed between the snow depth estimates and the field measurements. Further, the snow depth estimates from the proposed approach were observed to be significantly better than the DInSAR displacements based on the correlation with respect to the field measurements.

COMPARISON OF PSI AND DINSAR APPROACH FOR THE SUBSIDENCE MONITORING CAUSED BY COAL MINING EXPLOITATION

Abstract. Underground coal exploitation has its reflection in ground movements such as subsidence, sinking or shaking. These cause buildings and infrastructure damage, therefore it is important to measure the magnitude of deformation. Last decades, Differential Interferometric Synthetic Aperture Radar (DInSAR) captured considerable attention as a tool for deformation monitoring. The results of conventional DInSAR, which utilizes two SAR images, are degraded due to atmospheric, topographic and orbital errors. To overcome these limitations, various stacking-based methods have been introduced. Therefore, the goal of presented study is to compare Persistent Scatterer Interferometry (PSI) as stacking-based method with classical DInSAR for monitoring of subsidence caused by underground coal exploitation. Deformations in the areas of active mining exploitation are characterised typically by rapid non-linear movement. The comparison has been performed for the area of active exploitation in Rydułtowy mine located in Upper Silesian Coal Basin (USCB) in Poland. Results from two separate PSI and DInSAR processing portray similar deformation pattern over the study area. Unfortunately, due to the temporal decorrelation, PSI clearly demonstrate smaller information coverage in respect to DInSAR results. Additionally, due to the applied linear deformation model, PSI failed in displacement estimation with magnitude higher than 12 cm. In contrast, DInSAR thanks 6-day temporal baseline and no assumption for a deformation model, was able to capture the maximum magnitude of subsidence reaching 86 cm/year. However, these results are affected by atmospheric artefacts which in presented case study can reach even 14 cm/year. To achieve few cm level of accuracy and to estimate high deformation magnitude such as in presented study case (1m/year), integrated use of both InSAR techniques seems to be the reasonable solution.