ASAR Images Research Articles

A Localized Particle Filtering Approach to Advance Flood Frequency Estimation at Large Scale Using Satellite Synthetic Aperture Radar Image Collection and Hydrodynamic Modelling

This study describes a method that combines synthetic aperture radar (SAR) data with shallow-water modeling to estimate flood hazards at a local level. The method uses particle filtering to integrate flood probability maps derived from SAR imagery with simulated flood maps for various flood return periods within specific river sub-catchments. We tested this method in a section of the Severn River basin in the UK. Our research involves 11 SAR flood observations from ENVISAT ASAR images, an ensemble of 15 particles representing various pre-computed flood scenarios, and 4 masks of spatial units corresponding to different river segmentations. Empirical results yield maps of maximum flood extent with associated return periods, reflecting the local characteristics of the river. The results are validated through a quantitative comparison approach, demonstrating that our method improves the accuracy of flood extent and scenario estimation. This provides spatially distributed return periods in sub-catchments, making flood hazard monitoring effective at a local scale.

Reconstruction of spatially continuous time-series land subsidence based on PS-InSAR and improved MLS-SVR in Beijing Plain area

ABSTRACT Beijing has undergone severe settlement in recent years. Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technique has been widely used to derive time-series land deformation. However, existing studies have faced two challenges: (1) the nonlinear characteristics of time-series subsidence has not been fully investigated; (2) since PS points are normally distributed in urban areas with high building density, measurement gaps usually exist in nonurban areas. To address the challenges, we presented a new method to reconstruct spatially continuous time-series deformation. First, PS-InSAR was used to retrieve the deformation based on 135 scenes of Envisat ASAR and Radarsat-2 images from 2003 to 2020. Polynomial Curve Fitting (PCF) was then used to model nonlinear time-series deformation for the PS points. In the PS measurement gaps, Iterative Self-Organizing Data Analysis Technique (ISODATA) and Multi-output Least Squares Support Vector Regression (MLS-SVR) were used to estimate the PCF coefficients and then time-series deformation considering 40 features including thickness of the compressible layers, annual groundwater level, etc. The major results showed that (1) compared to linear, quadratic, and quartic models, cubic polynomial model generated better fit for the time-series deformation (R2 ≈0.99), suggesting obvious nonlinear temporal pattern of deformation; (2) the time-series deformation over measurement gaps reconstructed by ISODATA and MLS-SVR had satisfactory accuracy (R2 = 0.92, MAPE < 15%) and yielded higher accuracy (R2 = 0.947) than IDW (R2 = 0.687) and Ordinary Kriging (R2 = 0.688) interpolation methods. The reconstructed results maintain the nonlinear characteristics and ensure the high spatial resolution (120 m) of time-series deformation. Among the 40 predictor variables, ground water level datasets are the most influential predictors of time-series deformation.

Land subsidence prediction model based on its influencing factors and machine learning methods

Land subsidence has caused huge economic losses in the Beijing plains (BP) since 1980s. Building land subsidence prediction models that can predict the development of land subsidence is of great significance for improving the safety of cities and reducing economic losses in Eastern Beijing plains. The pattern of evolution of land subsidence is affected by many factors including groundwater level in different aquifers, thicknesses of compressible layers, and static and dynamic loads caused by urban construction. First, we used the small baseline subset Interferometric Synthetic Aperture Radar (SBAS‐InSAR) technology on 47 ENVISAT ASAR images and 48 RADARSAT‐2 images and used Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology on 27 Sentinel-1 images to obtain the land subsidence monitoring results from June 2003 to September 2018. Second, the accuracy of the InSAR monitoring results was validated by using leveling benchmark land subsidence monitoring results. Finally, we built land subsidence rate prediction models and land subsidence gradient prediction models by combining land subsidence influencing factors and four machine learning methods including support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random forest (RF) and Extremely Randomized Trees (ERT). The findings show: (1) The InSAR monitoring results revealed that the Beijing Plain has experienced serious land subsidence during the 2003–2010, 2011–2015 and 2016–2018 periods. (2): The InSAR monitoring results agreed well with the leveling benchmark monitoring results with the Pearson correlation coefficients of two monitoring results were all greater than 0.95 during the 2003–2010, 2011–2015 and 2016–2018 periods, respectively. (3): We found that the land subsidence prediction based on ERT method is the optimal model among four land subsidence prediction models and that the prediction performance of land subsidence prediction model based on ERT method will be greatly improved when apply this prediction model in sub study areas where the land subsidence mechanism is similar owning to the similar hydrogeological parameters.

Poyang Lake Wetland Classification Using Time-Series ENVISAT ASAR Data and Beijing-1 Imagery

Beijing-1 and ENVISAT ASAR images were used to classify wetland aquatic macrophytes in terms of their plant functional types (PFTs) over the Poyang Lake region, China. Speckle noise filtering, systematic sensor calibration within the same polarization or between different polarizations, and accurate geo-registration were applied to the time-series SAR data. As a result, time-series backscattering data, which is described as permittivity curves in this paper, were obtained. In addition, time-series indices, described as phenological curves, were derived from Beijing-1 time-series images in the classification experiment. Based on these two curves, a rule-based classification strategy was developed to extract wetland information from the combined SAR and optical data. In the rule-based wetland classification method, DEM data, submersion time index, temporal Beijing-1 images, time-series normalized difference vegetation index (TSNDVI) images, principal component analysis (PCA), and temporal ratio of ASAR time-series images were used. In addition, a decision tree-based method was used to map the wetlands. Conclusions include the following: (1) after the preprocessing of ASAR data, it was possible to satisfactorily separate different aquatic plant functional types; (2) hydrophytes from different PFTs exhibited distinct phenological, structural, moisture, and roughness characteristics due to the impact of the annual inundation of Poyang Lake wetland; and (3) more accurate results were obtained with the rule-based method than the decision tree (DT) method. Producer’s and user’s accuracy calculated from test samples in the classification results indicate that the DT method can potentially be used for mapping aquatic PFTs, with overall producer’s accuracy exceeding 80% and higher user’s accuracy for aquatic bed wetland PFTs. A comparison of producer’s and user’s accuracy from the rule-based classification increased from 3 to 12% and 7 to 26%, respectively, for different aquatic PFTs.

Time Series Multi-Sensors of Interferometry Synthetic Aperture Radar for Monitoring Ground Deformation

A stack of images is a prerequisite for the multi-temporal interferometric synthetic aperture radar (MT-InSAR) due to the wrapped nature of the interferometric phase. Although the SBAS technique can relieve the requirement of the amount of SAR data, dozens of SAR acquisitions could be regarded as the minimum requirement. However, due to the limitation of the imaging capability of the spaceborne SAR system, the amount of available SAR data acquired from only one SAR sensor is often not enough to satisfy the requirement for phase unwrapping based on the Nyquist sampling assumption. Fortunately, there sometimes may be more than one SAR stack, that is, stacks of SAR data acquired from different SAR systems. In this study, we propose a methodology to detect ground deformation by combining multiple SAR images acquired from different satellite systems for MT-InSAR analysis. First, the low-pass deformation is estimated based on time series SAR acquisitions with low spatial resolution and long wavelengths such as ENVISAT ASAR (ASAR). This information is then incorporated into the processing of time series of SAR acquisitions with high spatial resolution and short wavelength, such as TerraSAR-X (TSX). Specifically, the low-pass deformation will be subtracted from each differential interferogram generated from short-wavelength SAR images, and the rest of the MT-InSAR analysis will be based on the double-differentiation interferograms. Then, the residual deformation will be calculated from these double-differentiation interferograms and together with the low-pass deformation forms the full deformation. As the principal component of deformation has already been subtracted, the phase gradient of those double-differentiated interferograms will be smooth enough to facilitate the phase unwrapping. Between January 2009 and September 2010, 14 ASAR images and 11 TSX images acquired from Tianjin, China are selected as the test data. A root means square error (RMSE) of 9.1 mm/year is achieved from 11 TSX images, while a root means square error of 3.7 mm/year is achieved from 14 ASAR images. However, an RMSE of 1.6 mm/year is achieved when integrating 11 TSX images and 14 ASAR images for MT-InSAR analysis. The experiments show that the proposed method can effectively detect ground deformation.

Monitoring of Maskun landslide and determining its quantitative relationship to different climatic conditions using D-InSAR and PSI techniques

Climate change has resulted in severe landslides in Maskun, Iran. This study evaluates and monitors the displacement caused by the landslide mass in Bam using Interferometry Synthetic Aperture Radar (DInSAR) and Persistent Scatterer Interferometry (PSI) techniques, as well as identifies relationships between climatic conditions and mass displacement. Temperature and precipitation data from 2007 to 2019 were combined with satellite images and the DInSAR method was used to determine the mass displacement differences after selecting eighteen radar images from the ASAR sensor of the ENVISAT satellite. Additionally, Sentinel 1 satellite images were acquired and analyzed using the PSI method from November 5, 2014, to June 24, 2019. The highest displacement level at the surface of the Maskun landslide mass was then extracted. The ASAR images show a monthly displacement rate of 7.3 mm. The smallest displacement, on the other hand, occurred between May and September 2009, at a rate of 3.1 mm/month. PSI results also revealed that the maximum Line Of Sight (LOS) velocities detected by PSI are −64.5 mm/yr (away from the satellite) and 32.45 mm/yr (toward the satellite). Rainfall is one of the main triggers for increasing the deformation of the Maskun landslide according to the time-series analysis.

Prediction of InSAR deformation time-series using a long short-term memory neural network

ABSTRACT The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.

Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis

Landslide susceptibility maps (LSMs) are generally prepared by integrating multiple prominent thematic layers, including DEM derived products (elevation, slope, and aspect), and other parameters such as lithology, geomorphology, LULC, etc. These parameters can be assigned optimum weights using the analytic hierarchy process (AHP) method, followed by a GIS-based weighted overlay analysis. In recent years, multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been rigorously explored, for land deformation detection and monitoring, by extracting highly stable measurement pixels using tens of SAR acquisitions simultaneously. In this research work, a GIS-based multi-criteria decision analysis to prepare LSMs is proposed, with MT-InSAR derived displacement estimates used as a critical input parameter. An LSM is generated by processing 20 ERS-1/2 and Envisat ASAR images, acquired over ∼120 sq. km wide river basin, located in Uttarakhand, India. The generated LSM is found to be congruent with the susceptible maps made available by the Geological Survey of India (GSI) under the National Landslide Susceptibility Mapping (NLSM) program. Preliminary results indicate that the majority of the unstable zones along the Alaknanda River are correctly identified. The approach is further implemented to generate an updated susceptibility map using 60 scenes of freely available Sentinel-1A dataset, followed by validation through actual field survey. This resulted in the generation of an updated susceptibility map, which helped in the identification of 44.5% new landslide susceptible zones (LSZs). Furthermore, the status of previously identified zones is also quantified. The performance of the proposed approach suggests its usability in generating and updating near-real-time LSMs.

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.

Analysis of the Contribution Rate of the Influencing Factors to Land Subsidence in the Eastern Beijing Plain, China Based on Extremely Randomized Trees (ERT) Method

As a common geological hazard, land subsidence is widely distributed in the Eastern Beijing Plain. The pattern of evolution of this geological phenomenon is controlled by many factors, including groundwater level change in different aquifers, compressible layers of different thicknesses, and static and dynamic loads. First, based on the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique, we employed 47 ENVISAT ASAR images and 48 RADARSAT-2 images to acquire the ground deformation of the Beijing Plain from June 2003 to November 2015 and then validated the results using leveling benchmark monitoring. Second, we innovatively calculated additional stress to obtain static and dynamic load information. Finally, we evaluated the contribution rate of the influencing factors to land subsidence by using the Spearman’s rank correlation coefficient (SRCC) and extremely randomized trees (ERT) machine learning methods. The SBAS-InSAR outcomes revealed that the maximum deformation rate was 110.7 mm/year from 2003 to 2010 and 144.4 mm/year from 2010 to 2015. The SBAS-InSAR results agreed well with the leveling benchmark monitoring results; the correlation coefficients were 0.97 and 0.96 during the 2003–2010 and 2013–2015 periods, respectively. The contribution rate of the second confined aquifer to the cumulative land subsidence was 49.3% from 2003 to 2010, accounting for the largest proportion; however, its contribution rate decreased to 23.4% from 2010 to 2015. The contribution rate of the third confined aquifer to the cumulative land subsidence increased from 2003 to 2015. Although the contribution of additional stress engendered from static and dynamic loads to the cumulative land subsidence was slight, it had a significant effect on the uneven land subsidence, with a contribution rate of 33.8% from 2003 to 2010 and 23.1% from 2010 to 2015. These findings provide scientific support for mitigating hazards associated with land subsidence.

Designing a GPU-parallel algorithm for raw SAR data compression: A focus on parallel performance estimation

When a Synthetic Aperture Radar (SAR) acquires raw data using a satellite or airborne platform, it must be transferred to the ground for further processing. For example, SAR raw data need a so-called ’focusing’ signal processing to render it into a visible image. Such processing is time and computing consuming, and it is commonly carried out in computing centres. Since the data transfer rate is a typical limitation when communicating with the ground station, compression is necessary to reduce transmission time. So far, this procedure has been implemented in application-specific hardware, but recent adoption of avionic computational GPUs opened to new high-performance onboard perspectives. Due to the limited availability of avionic GPUs, we focused on parallel performance estimation starting from measures relative to a similar off-the-shelf solution. In this paper, we present a GPU algorithm for raw SAR data compression, which uses 1-dimensional DCT transforms, followed by quantisation and entropy coding. We evaluate results using ENVISAT (Environmental Satellite) ASAR Image Mode level 0 data by measuring compression rates, statistical parameters, and distortion on decompressed and then focused images. Moreover, by evaluating the Algorithmic Overhead induced by the parallelisation strategy, we predict the best thread-block configuration for possible adoption of such a GPU algorithm on one of the most available avionic hardware.

An investigation of surface deformation over oilfield in Southwest Iran (2003–2010) using InSAR and physical modelling

ABSTRACT Land subsidence is one of the most hazardous phenomena. Rapid rate subsidence corresponds to human activities such as subsurface withdrawal. Therefore, local changes in surface elevation and its associated response has the potential to damage the industrial structures. In this research, the small baseline subset (SBAS) algorithm is applied to perform an Interferometric Synthetic Aperture Radar (InSAR) time series analysis within 2003 and 2010 over a giant oilfield located in the Zagros fold-thrust belt in southwest of Iran. Thirty-one interferograms are generated using 18 Envisat ASAR images. The mean velocity map obtained in the Line-Of-Sight (LOS) direction of satellite to the ground reveals the maximum subsidence on order of 17.3 mm per year over the field due to both tectonic and non-tectonic features. In order to assess the effect of non-tectonic features such as petroleum exploitation on surface displacement, the results of InSAR have been compared with the oil production rate. Also, a physical model of reservoir pattern has been developed using 2D seismic profiles as the way to estimate tectonic deformation which indicates the influence of depth variations of the Gachsaran salt decollement on structural style.

Urban subsidence monitoring by SBAS-InSAR technique with multi-platform SAR images: a case study of Beijing Plain, China

ABSTRACT Beijing, as the capital of China, suffers severe subsidence caused by groundwater withdrawal. In this paper, the SBAS-InSAR technique was used to detect surface deformations in the Beijing plain. 47 scenes Envisat ASAR images obtained during June 2003 to August 2010, and 24 Radarsat-2 images obtained during November 2010 to December 2013 were employed to retrieve vertical deformation characteristic over this area. The deformation magnitude, evolution process and spatial distribution of representative targets that occurred during 11 years were retrieved. The vertical surface displacement rates during two periods were validated via leveling observations, which have an RMSE =1.15mm/a, which indicates that the two types of subsidence matched very well. The interferometric results revealed that the subsidence during the two periods show similar special distributions and have an uneven spatial-temporal trend. The scope and deformation rates of funnels continuously increased with from 2003 to 2013. Meanwhile, multiple expressways and urban ring roads spanned the subsidence funnel with sharp deformation rate, and showed different subsidence properties along their paths. Additionally, the spatial and temporal correlation analyses between the deformation measurements and the dynamic variation of groundwater pumping was also performed to explore the response relationship.

MONITORING AND ANALYZING THE SURFACE SUBSIDENCE IN LANGFANG BY TIME-SERIES INTERFEROMETRIC RADAR TECHNIQUE

Abstract. In this paper, the subsidence change maps in Langfang were obtained using ASAR images covering Langfang area acquired from August 2007 to September 2010 with the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique. Surface subsidence spatial-temporal characteristics in Langfang was investigated. The analysis of experimental results show that 1) prominent uneven subsidence patterns were identified in Langfang. Specifically, surface subsidence rates ranged from −77 mm/year to 4 mm/year, and maximum subsidence rate was detected in Nanjianta Town; 2) the standard deviation of the target point subsidence rates in the study area ranges from 0.8 mm/year to 9.3 mm/year, 70.4% of the monitoring point standard deviations in study area are less than 3 mm/year; 3) surface subsidence time series presented nonlinear variation trend. The surface subsidence has a tendency to gradually expand around the center of Nanjianta Town, and the scope and trend of expansion to the north and west of Langfang are more serious.

Land Subsidence Vulnerability Assessment of Rural Settlements in Fars Province

Purpose- Land subsidence is caused by natural factors and human activities around the world. Fars Province, located in the south of Iran, is subject to land subsidence due to the uncontrolled exploitation of groundwater, causing damages to the population and human settlements and also environmental, social and economic areas. Design/methodology/approach- The present research is descriptive in terms of describing land subsidence in the case study region, whereas it is also analytical as time series analysis techniques based on Radar Interferometry (InSAR) is applied to monitor temporal changes in subsidence in Darab and Fasa Plains, including 470 rural points. Using 8 ENVISAT ASAR images spanning between 2005 and 2010, nine Interferograms were processed. In the study area. Geographic Information System (GIS) is then used to study groundwater level decline at the well locations in a 24-year period (from 1991 to 2015). Findings- The results of the research confirm that there is a significant correlation between groundwater water level decline and land surface subsidence. Time series analysis of the processed Interferograms indicate the mean displacement velocity map, demonstrating the maximum subsidence rate of 25 cm/yr. The InSAR analysis reveal within the study area subsidence rate of 25 cm/year in 24 years period and locally exceeding 30 cm/yr in the last decade. This area of significant subsidence is limited in its spatial extent to the agricultural land and is partly influenced by the large-scale over-exploitation of groundwater resources in the region study. The temporal and areal relationships of subsidence and groundwater level data suggest that a significant part of the observed subsidence in the Darab region is caused by intense groundwater extraction which has led to widespread compaction within the upper parts of the up to 300m. Socioeconomic analysis and the subsidence hazard map show that 105523 people are generally at risk of subsidence, of 65068 who are at high risk. In addition, there are 2679 socioeconomic infrastructures such as public service at risk of damage by land subsidence. Research limitations/implications- Limitation in In SAR data access, especially for long-term data was one of the main limitations in land subsidence research and also in this research. Practical implications- Integrated water resource management and the observed extraction of groundwater could influence the subsidence rate in the regions exposed to land subsidence. Originality/value- This research will be important to provide vulnerability in regions with groundwater overexploitation.

Surface Deformation over Permafrost Environment of the Tanggula Section in the Qinghai-Tibet Plateau Using Active Microwave Spectral Imagines

Changes of permafrost and the active layer affect geophysical process, water circulation, biological diversity and carbon storage. The monitoring of ground surface deformations is crucial and necessary to estimate the degradation of permafrost environment. In this study, the ground motions of the Tanggula section in the Qinghai- Tibet Plateau (QTP), a region with limited in situ measurements at spatial scale, have been investigated by the small baseline SAR interferometry (SBAS-InSAR). In total, 17 C-band ENVISAT ASAR images (acquired from January 1, 2009 to August 19, 2010) were employed. The results show that the ground surface motion was primarily in the range of – 7.6 mm per year to 6.7 mm per year, and the correlation coefficients and the P-values between deformations and Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperatures (LSTs) were ranged from -0.24 to -0.47 and from 0.09 to 0.41, respectively. And the precision file of the surface displacement rates map is showed that the estimated displacement velocity is reliable. Our study has observed that the majority of ground surface (approximately 70%) was relatively stable in the study period with displacements in the range of -2 mm per year to 2 mm per year and the ground subsidence is larger than the ground heave, implying the necessity of time series deformations surveillance derived from satellites in the permafrost environment.

IDENTIFICATION OF FLOODED AREAS DUE TO SEVERE STORM USING ENVISAT ASAR DATA AND NEURAL NETWORKS

The specific objective of the present study is to identify flooded areas due to cyclonic storm using Envisat ASAR VV polarized data and Artificial Neural Network (ANN). On October 30, 2006, the Ogni storm crossed the Indian coast. It impacted three coastal districts in Andhra Pradesh, including Guntur, Prakasam, and Krishna. The present study considers only nine mandals of Guntur district of Andhra Pradesh for identification of flooded areas. For this purpose, pre and post event images of study area were procured of Envisat satellite (April 23, 2006 and November 4, 2006). Field visit to the affected district after the disaster was carried out to gather landcover information. In all, 564 pixels landcover information was collected during the visit (These were corresponding to pre event Envisat image of April 23, 2006). Out of the 564 pixels, randomly 406 pixels (91 were water and the remaining 315 were non-water pixels) were used for training the Neural Network and the remaining for testing. Using the trained ANN model, the total water area in the nine mandals of Guntur using Envisat ASAR satellite imagery of April 23, 2006 was found to be 2.344 thousand hectares. The trained model was applied to the post event Envisat ASAR image of November 4, 2006 to obtain completely submerged and partial/non submerged areas under water. The completely submerged landcover under water in nine mandals of Guntur district on November 4, 2006 was found to be 13.2705 thousand hectares. Results suggest a high accuracy of classification and indicate that this may be a rapid tool for damage estimation and post disaster relief and recovery efforts.

Reactivation of the Adıyaman Fault (Turkey) through the Mw 5.7 2007 Sivrice earthquake: An oblique listric normal faulting within the Arabian-Anatolian plate boundary observed by InSAR

On February 21, 2007, a moderate-sized (Mw 5.7) earthquake struck the town of Sivrice (Elazig, Turkey) located within the East Anatolian Fault (EAF) zone that forms the boundary between the Arabian and Anatolian plates. The earthquake source parameters of the mainshock reported by different agencies are significantly different. In the mean time, the relation of this earthquake to the EAF has not been fully explored. In this study, we combine remotely sensed Synthetic Aperture Radar data obtained from ENVISAT ASAR images (European Space Agency) with relocated seismicity to map the observed surface displacement field, resolve the earthquake source parameters and determine the fault plane geometry. We calculated coseismic interferograms from both ascending and descending orbits and modeled them by elastic dislocations on rectangular fault surfaces using a downhill simplex simulated annealing algorithm. InSAR analysis and seismicity distribution reveal that the earthquake took place on the Adıyaman fault (AF), a major southern splay of the EAF. The ruptured part of the AF has a listric geometry with an oblique normal slip (rake −73±21°), and a strike of N42°E. The computed coseismic slip is 64±18 cm with a moment magnitude of Mw 5.9. The resolved fault plane has a steep dip (greater than 80°) near the surface and mildly dipping at depths between 3.6 and 8.5 km (dip 63±4°). The kinematics of the faulting is supported by the observed transtensional left-lateral strike-slip regime in the region of tectonic depression of Hazar Lake.

Geological parameters affected land subsidence in Mashhad plain, north-east of Iran

Mashhad plain is an area with a dry-to-semi-dry climate in Razavi Khorasan Province, Iran. About 60% of 6 million population of the province live in this plain, and mainly in Mashhad city. Overexploitation of ground water during the past decades has led to a severe drop in groundwater table and land subsidence. In this respect, some parts of the plain show an annual groundwater-level decline of 1.5 m. The surface subsidence features such as rising of the well casing, well collapse, and tension cracks observed in different parts of the area. Studying of ENVISAT ASAR images in a period of 2003/06/30 to 2010/05/24 shows the maximum subsidence rate of 32 cm/year in the north-west of the Mashhad city, but there was a mismatch between groundwater drop and ground subsidence in some parts of the plain. The evaluation of geological characteristics of subsidence area reveals, in addition to the groundwater decline, tectonic condition and active faults which change the bedrock depth have a key role in the pattern and rate of subsidence. Also, consolidation of the Pleistocene marl bedrock located in the central part of the plain is effective in the increased subsidence rate of the study area.

Measurement of the three-dimensional surface deformation of the Jiaju landslide using a surface-parallel flow model

ABSTRACTSAR Interferometry (InSAR) is a powerful technique for mapping land surface deformation, but single one-dimensional (1D) InSAR line-of-sight (LOS) measurement limits its application in three-dimensional (3D) displacement retrieval. Traditional methods were developed for measuring large-scale deformation. In this paper, to retrieve the slow 3D deformation process in the Jiaju landslide, a surface-parallel flow model is applied, according to the existing geological data gained from this landslide. Nine ENVISAT ASAR images and 19 ALOS PALSAR images were collected to reconstruct the 3D deformation field. An iterative method for correcting the characteristic value (IMCCV) with maximum likelihood estimation (M-estimation) adaptively generates an accurate stochastic model. The 3D deformation measurements show that the northern part of the Jiaju landslide clearly had horizontal deformation with the largest deformation velocity exceeding 15 cm year−1, while the southern part was relatively stable. Similarly, large vertical deformation appeared in the northern part (−2 cm year−1) and little deformation occurred in the southern part (−0.5 cm year−1). Compared with GPS measurements, the standard deviation is 1.4 cm year−1 and 0.7 cm year−1 in horizontal and vertical directions, respectively. Therefore, the Jiaju landslide exhibited movements in the horizontal direction accompanied by slight settlement in the vertical direction.