Conference Agenda

Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed: (i) The phase decorrelation caused by heavy vegetation can be weakened by use of SAR imagery with a long radar wavelength (e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution (e.g. 1 m or even higher), and/or advanced InSAR time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry (RSSI) techniques; (ii) Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The Generic Atmospheric Correction Online Service (GACOS) developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis; (iii) The geometric distortions such as shadows and layovers can be pre-analyzed using an external DEM for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM; and (iv) Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e.g. Ground-based radar, Lidar and GNSS) to develop an automated landslide detection and monitoring system. It is hoped that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.

We used SBAS (Small BAseline Subset) method to have obtained the cumulative surface deformation at some high coherent points for different time (each 12 days interval from September 10, 2017 to June 1, 2018.) after the 6^th nuclear explosion of Democratic People’s Republic of Korea (DPRK) in the 17 km*22 km range of the center of this explosion event. These measured points are aggregated into 14 sets according to their spatial neighbourhood. The cumulative deformation of each set is computed by weighted averaging the deformation of each points inside the set according to their coherence. The relationship of the cumulative deformation of different sets with time processes is fitted by using Weibull model. Furtherly, the spatial analysis of the maximum vertical deformation has been carried out, it has been determined that the driving force of the sink was the gravity of the upper rock and itself which became soften and crisp under the action of high temperature and high pressure released from the nuclear explosion. The influencing factors are modeled and analyzed. The results show that by using SBAS-InSAR, the deformation process in the thermal radiation aftereffect stage of the 6th nuclear test could be effectively observed. Surface uplift still existed near the epicenter during about 10 days after the explosion, and then began to sink. The sinking rate and total sinking amount are different in different places. Meanwhile, the phenomenon of subsidence slowing down or even uplifting caused by freeze-thaw of water in underground rock in winter has been observed. After May 24, 2018, deformation began to rise due to the government of DPRK bombed the entrance of the nuclear facilities. The results of modeling analysis are as follows: 1) The InSAR data acquired in short revisit period can be used to observe the deformation process after the DPRK‘s 6th nuclear test and the freeze-thaw deformation process of rock crevice water in winter and spring. 2) In the thermal radiation aftereffect stage of the DPRK’s sixth nuclear expolsion, the surrounding rock has been softened under high temperature and high pressure, then the surrounding metamorphic rock was compressed under the action of own gravity and began to sink. This time-varying process can be model with Weibull function. 3) Considering the factors such as the layer thickness of metamorphic rock and the distance from epicenter, modeling the spatial distribution relationship of maximum cumulative vertical deformation. The following results has been drawn: the maximum vertical impact distance of the explosion from epicenter is about 2000 meters, and the deformation coefficient of the metamorphic rock is about 7*10^-5, the statistical fitting degree is about 0.8, and the confidence closes to 1.

On 21 September 1999, the Mw 7.6 Chi-Chi earthquake, one of the largest inland earthquakes in Taiwan happened and struck the Taipei Basin, in the Central western part of the island, killing more than 2400 people and damaging 100 000 structures. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. Revisiting this earthquake with a range of earth observations will allow better understanding of regional fault properties. ERS images from the descending track 232 and covering the period from 21 January 1999 to 28 October 1999 were interferometrically processed using the ESA open-source software SNAP to investigate the co-seismic deformation. With InSAR, only the footwall can be analysed because the hanging-wall, which likely experienced the main deformation in this event, is densely vegetated resulting in low coherence in the interferograms. Co-seismic interferograms show about 10-11 fringes in the footwall which is equivalent to a surface displacement of up to approximately 30 cm. In order to obtain observations of the hanging-wall, Cosi-Corr software was used to correlate pre and post SPOT optical images. In addition to these two datasets, GNSS and leveling data were also used. PSOKINV (Particle Swarm Optimization and Okada Inversion package), a geodetic inversion package, was used to determine the fault geometry and the slip distribution. Firstly, the relative weights of the four datasets were determined using the generalized Akaike’s Bayesian Information Criterion (gABIC). Secondly, the Particle Swarm Optimization (PSO) was utilised in the geodetic modelling to determine an optimal uniform model with 4 fault segments. Thirdly, a joint inversion of InSAR and geodetic data (SPOT, GNSS and leveling) was realised to estimate the slip distribution. These datasets enabled us to get information about the hanging-wall of the fault and to improve the modelling.

Tropospheric phase delays (TPDs) are a dominating error source in InSAR measurements. External atmospheric observations from, e.g., GNSS (Global Navigation Satellite Systems) have been used to correct the effects of TPDs on InSAR measurements. The spatial and temporal resolutions of such external data are however often not enough to accurately estimate the TPDs. We propose a multi-temporal InSAR data processing model that jointly estimates TPDs, ground deformation, and residual topographic errors. The spatial variability of the relationship between TPDs and topographic height is considered by using localized estimation windows formed according to height gradients. We demonstrate the performance of the proposed method by using both simulated and real datasets from ALOS/PALSAR and Sentinel-1 images.

Over the last few years, several techniques have been developed for monitoring dam displacements and water surface levels. The use of Global Navigation Satellite System (GNSS) allows us to determine the displacements of a dam, located in southern Italy, along the orthogonal direction, while remote sensing techniques are used to retrieve the reservoir levels.

The latter have been evaluated by using different strategies involving the use of a consistent dataset of optical and Synthetic Aperture Radar (SAR) images with different spatial and radiometric resolution. Initially, a preliminary comparison between the water’s edge and the existing contour lines and the use of unsupervised classification have been tested. Subsequently, two other Object-Based Image Analysis (OBIA) were performed on the dataset, one based on the use of four similarity indices, the other based on the evaluation of the distance between the water’s edge and the contour lines.

The dam displacements were retrieved using the static positioning involving a GNSS receiver on the top of the dam and a Continuously Operating Reference Station (CORS), approximately 30 km away. Measured displacements over the dam and the surrounding area have employed Interferometric SAR (InSAR) techniques which have been evaluated, using different Multi-Baseline Construction methods applied to Sentinel-1A TOPS-SAR dataset to test the accuracy of the techniques over extra-urban areas. Preliminary results show that the behaviour of the dam, in terms of displacements, is related to reservoir levels but also to meteorological effects.

3D SAR Tomography (TomoSAR) [1-4] and 4D SAR Differential Tomography (Diff-TomoSAR) [8-14] exploit multi-baseline SAR data stacks to create an important new innovation for SAR Interferometry, to sense complex scenes with multiple scatterers mapped into the same SAR range cell. In addition to 3-D shape reconstruction and resolving deformation in complex urban/infrastructure areas [2,4], and recent cryospheric ice investigations [5], emerging tomographic remote sensing applications include forest scenarios [3,6,7], e.g. tree height and biomass estimation, sub-canopy topographic mapping, and even search, rescue and surveillance. However, often these scenes are characterized by temporal decorrelation of scatterers, orbital, tropospheric and ionospheric phase distortion and an open issue regarding possible height blurring and accuracy losses for TomoSAR applications particularly in densely vegetated mountainous rural areas. Thus, it is important to enhance characterisations of temporal decorrelation, orbital, tropospheric and ionospheric phase distortion.

We report here on 3D imaging (especially of vertical layers) over densely vegetated mountainous rural areas using 3-D SAR imaging (SAR tomography) derived from data stacks of X-band COSMO-SkyMed Spotlight and L band ALOS-1 PALSAR data stacks over Dujiangyan Dam, Sichuan, China. A new TanDEM-X 12m DEM is first used to assist co - registration of all the data stacks. Then, orbit baseline estimation is introduced. Atmospheric correction is assessed using a weather model with inputs derived from ERA-I and GACOS which are compared alongside ionospheric correction methods to remove ionospheric delay. The Compressive sensing (CS) TomoSAR method with the TanDEM-X 12m DEM is described in order to obtain the number of scatterers inside each pixel, the scattering amplitude and phase of each scatterer and finally extract tomograms (imaging), their 3D positions and motion parameters (deformation). Examples will be demonstrated of 3D TomoSAR imaging results over Dujiangyan Dam, Sichuan, China as well as sample datasets from the ESA BioSAR 2008 L band data in Sweden (forest) and ALOS L band data in San Francisco Bay (urban building and bridge).

This work is partially supported by the CSC and UCL MAPS Dean prize through a PhD studentship at UCL-MSSL.

[1] A. Reigber, A. Moreira, “First Demonstration of Airborne SAR Tomography using Multibaseline L-band Data,” IEEE TGARS, 38(5), pp.2142-2152, 2000.

[2] G. Fornaro, F. Serafino, F. Soldovieri, “Three Dimensional Focusing With Multipass SAR Data,” IEEE TGARS, 41(3), pp. 507-517, 2003.

[3] M. Nannini, R. Scheiber, R. Horn, “Imaging of Targets Beneath Foliage with SAR Tomography,” EUSAR’2008.

[4] F. Lombardini, F. Cai, D. Pasculli, “Spaceborne 3-D SAR Tomography for Analyzing Garbled Urban Scenarios: Single-look Superresolution Advances and Experiments," IEEE JSTARS, 6(2), pp.960-968, 2013.

[5] L. Ferro-Famil, C. Leconte, F. Boutet, X. Phan, M. Gay, Y. Durand, “PoSAR: A VHR Tomographic GB-SAR System Application to Snow Cover 3-D Imaging at X and Ku Bands,” EuRAD’12.

[6] F. Lombardini, F. Cai, “3D Tomographic and Differential Tomographic Response to Partially Coherent Scenes,” IGARSS’08.

[7] M. Pardini, K. Papathanassiou, “Robust Estimation of the Vertical Structure of Forest with Coherence Tomography,” ESA PolInSAR ’11 Workshop.

[8] F. Lombardini, F. Cai, “Evolutions of Diff-Tomo for Sensing Subcanopy Deformations and Height-varying Temporal Coherence,” ESA Fringe’11 Workshop.

[9] F. Lombardini, “Differential Tomography: A New Framework for SAR Interferometry”, IEEE TGARS, 43(1), pp.37-44, 2005.

[10] Xiang, Zhu Xiao, and Richard Bamler. "Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm." In Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp. 17-20. IEEE, 2010.

[11] F. Lombardini, M. Pardini, “Superresolution Differential Tomography: Experiments on Identification of Multiple Scatterers in Spaceborne SAR Data,” IEEE TGARS, 50(4), pp.1117-1129, 2012.

[12] F. Lombardini, F. Viviani, F. Cai, F. Dini, “Forest Temporal Decorrelation: 3D Analyses and Processing in the Diff-Tomo Framework,” IGARSS’13.

[13] Tebaldini, S., & Rocca, F. (2012). Multibaseline polarimetric SAR tomography of a boreal forest at P-and L-bands. IEEE Transactions on Geoscience and Remote Sensing, 50(1), 232-246.

[14] Huang, Y., Ferro-Famil, L., & Reigber, A. (2012). Under-foliage object imaging using SAR tomography and polarimetric spectral estimators. IEEE transactions on geoscience and remote sensing, 50(6), 2213-2225.

On October 11, 2018, a landslide occurred at the junction of Baiyu County, Ganzi Prefecture, Sichuan Province, and Boro Township, Jiangda County, Changdu County, Tibet, resulting in the breakdown of the Jinsha River and the formation of a barrier lake. On November 3, 2018, the secondary landslide occurred at the original landslide site of Baige Village, Boro Township,which endangers the lives and property of people in Baiyu County, Batang County, Derong County and other downstream areas of Ganzi Prefecture, and poses a threat to many hydropower stations.

After the landslide, we collected and processed the Planet optical satellite images (with resolution of 3 m) from 29 August 2018 to 5 December 2018, and constructed the time series of landslide spatial distribution. The change of Baige landslide at this stage is analyzed. According to the Planet satellite images, there are obvious sliding signs in this area for a long time. The phenomenon of rock strata falling off and baring has appeared in the mountain area. The deformation before and after the landslide is measured to obtain the total deformation of the landslide. Meanwhile, we collected sentinel-1 and ALOS-2 satellite data and used D-InSAR technology to analyze landslide deformation information. For the incoherent region caused by rapid deformation, the Pixel Offset Tracking (POT) technique is introduced to analyze the deformation information of the deformation body edge. The results obtained from the two kinds of satellite data can better reflect the continuous deformation of the slope at an earlier stage. In the early stage of occurrence, the deformation characteristics of the whole slope of the landslide body are very obvious, and the deformation magnitude and range have increased significantly. Comparing with the high-resolution optical satellite landslide, it is found that the cumulative deformation is consistent with the deformation measured by optical remote sensing. After the secondary landslide, we also collected COSMO-SkyMed satellite data and monitored the landslide continuously. The results of deformation monitoring show that the trend of deformation in the landslide area decreases from November 8 to 23. The results of deformation monitoring can monitor the development and movement of landslide, and provide information support for landslide emergency response.

To avoid new landslides occurring in the upper and lower reaches of Baige landslide in Jinsha River Basin, Sentinel-1A data was used to survey and monitor the potential hazard points. 5 km buffer is used to extract the coherent point targets along the Jinsha River, and high-resolution optical image validation is used to interpret and verify the hidden point areas with obvious sliding signs. In the follow-up, in view of these key hidden dangerous areas, we will continue to use time-series SAR images to carry out key monitoring work, so as to achieve real-time dynamic monitoring of the latest deformation information of landslide hidden dangerous points.

We thank Beijing Global Nebula Remote Sensing Technology Co., Ltd. and Beijing Vastitude Technology co.,ltd for providing ALOS PALSAR, COSMO-SkyMed and Planet optical data. ESA is acknowledged for providing Sentinel-1 data.

It is well known that ground moving target indication (GMTI) using SAR image is based on the differences of SAR data characteristics between moving target and stationary background cluster. In an along-track interferogram, the phase of background cluster should be 0 while that of moving target not be. Therefore, GMTI could be performed by utilizing along track inteferometry (ATI) technology. However, for the real interferometric SAR images, there are many factors affecting ATI phase, which make the phase of most stationary background objects interfered rather than zero, resulting in interferometric phase confusion between moving and stationary targets. That makes it difficult to effectively indicate the moving targets from cluster by using ATI phase information alone.

In the past decades, it has become one of the research trends to comprehensively utilize the phase and amplitude of ATI for GMTI. Combining of constant false alarm rate (CFAR) and ATI is considered to be a promising method to improve the detection rate, referred to as ATI-CFAR method. Gao at al.(2015) proposed an ATI-CFAR method to furtherly improve the detection accuracy. Unlike a general ATI-CFAR method, it adds two steps to the processing flow: the coarse detection for purified background cluster before estimating parameters of cluster distribution model; and the filtering for interferometric amplitude and phase after ATI-CFAR detection. This method has been validated in their research of airborne SAR GMTI. Applying it for TerraSAR-X GMTI, however, the ATI phase threshold is easy to be overestimated, which leads to a large number of missed detection and even unable to detect moving targets. In this paper, Gao’s method has been improved in two aspects, which are mainly presented in: 1) introducing a priori knowledge about vehicle velocities into the estimation of interferometric phase threshold, so as to improve the detection rate of moving targets; 2) using the graphic analysis method for the proportion of strong scattering pixels in full-aperture image to make the estimation of the interferometric amplitude threshold more intuitive.

The main goal of this paper is to test the ability of detecting moving vehicle using ATI-CFAR and TerraSAR-X data. Based on the above improved ATI-CFAR method, a GMTI experiment is carried out on a section of Beijing's North Fifth Ring Road. TerraSAR-X data with DRA mode, including 1 full-aperture and 2 sub-aperture SAR images, was acquired on November 30, 2015. In-situ information related to the moving vehicles on target road was obtained through two ways: one is, information including the number, type and speed of vehicles, acquired by the ground video-recording of the testing area synchronized with TerraSAR-X satellite flying over; and the other is, the average speed of vehicles on the testing road, collected via navigation service by Baidu Company of China. The detection area in the SAR image, which is located in the Olympic Park in the south of the target road, that was determined by the offsetting in the azimuth direction based on the real vehicle velocities. By comparing the two kinds of speeds derived from ATI phase and offsetting in the azimuth direction, the 14 among 16 moving targets detected are considered to be reasonable vehicles, and their average speed is accordingly comparable with in-situ vehicle velocities both from video-recording and Baidu. Then the detection rate is up to 70%, and the correctness of detection is about 88%. The experimental results show that the improved ATI-CFAR method can effectively detect moving vehicles in the TerraSAR-X images.

The authors would like to thank German Aerospace Center (DLR) for providing the TerraSAR-X DRA data（ATI_TRAF6781）.

Abstract：It is an effective way to reduce casualties by obtaining earthquake-induced building damage information accurately and rapidly. However, traditional methods mainly depend on in-depth field investigation to obtain seismic disaster information, which have some shortcomings, such as time-consuming, heavy workload and poor timeliness. Comparing to traditional methods, Synthetic Aperture Radar (SAR) remote sensing overcomes the above shortages, playing an important role in disaster assessment by means of its all-day and all-weather capability.

European Space Agency (ESA) provides Sentinel-1A SAR data which are widely used to derive global disaster information. The 2016 Italy earthquake, in which a large number of buildings collapsed and 299 people died was taken as study case of this paper. Three Sentinel-1A VV and VH dual-polarization images are obtained. Two of them are pre-event and one is post-event.

The method to detect building damage has three steps as follows. Firstly, intensity and coherence are derived from data preprocessing and are calculated into normalized difference respectively. In order to fully use polarization features, combine VV and VH to obtain mean of normalized intensity difference and of normalized coherence difference. Secondly, this paper selects some samples of damaged and intact buildings randomly, acquiring corresponding mean of normalized intensity and coherence and built a new discriminant function. It can classify all collapsed and intact buildings of the study area by setting a threshold value. Finally, validation data are derived from visual interpretation of high resolution optical images and are used to evaluate the accuracy of the method.

The result reveals that the method can evaluate damaged and intact buildings accurately and accuracy of the method is up to 81%. However, the result displays two anomalies because a lot of cars and tents for rescuing are together, which are taken as damaged buildings. The method can satisfy the timeliness of post-earthquake disaster assessment and accurately evaluate the spatial distribution of damaged and intact buildings, which has great potential in guiding rapid rescue.

Key words: Building damage assessment; Dual polarization; Sentinel-1A; SAR; Discriminant function

Since the launch of Sentinel-1 (S1) satellite in 2014, the public freely available Sentinel-1 SAR data has been widely used in ground deformation mapping. The Interferometric Wide-swath (IW) mode, as the main operation mode of Sentinel-1 mission, utilizes the Terrain Observation by Progressive Scan (TOPS) technique to achieve wide-swath coverage and short revisit interval at the cost of lower azimuth resolution and smaller Doppler bandwidth. This leads to lower accuracy of along-track measurements using conventional Multi-Aperture Interferometry (MAI) or Offset Tracking techniques, which limits the capability of Sentinel-1 TOPS data in three-dimensional (3D) monitoring of land subsidence.

However, the large squint angle diversity of ~1° between consecutive bursts of TOPS mode provides an opportunity of using modified MAI / Spectral Diversity (SD) techniques in burst overlap regions to retrieve along-track displacements with much higher accuracy. This method, referred to as Burst Overlap Interferometry (BOI), has been applied to measure large-scale earthquakes with metre-level displacement rate, but has not yet been assessed in time series analysis of slow deformation.

This study aims to evaluate the use of Burst Overlap Interferometry with Sentinel-1 time series TOPS images for along-track measurements of millimetre-level land subsidence induced by land reclamation and recent subway construction in the city of Shenzhen, China. The feasibility of reconstructing the along-track displacement field in the non-overlap regions between consecutive bursts by interpolation methods will be investigated in the case of small-scale and slow surface motion.

This work has been supported by the National Key Research and Development Program of China (Project ID. 2017YFB0504200) and National Natural Science Foundation of China (Project ID. 41801360). This research is linked to the ESA-MOST DRAGON-4 Project #32244: Earth observations for geohazard monitoring and risk assessment.

Landslide monitoring activities are of paramount importance for landslides hazard and risk assessment. Ground-based interferometric radar (GBIR) is a revolutionary advanced measurement technique for geoscience and engineering geodesy. It is powerful for temporally and spatially dense measurements of the highly dynamic target with sub-millimetric accuracy. GBIR has already been successfully used to identify and classify landslides, that can be considered complementary or alternative to space-borne SAR interferometry for terrain monitoring.

The Xuyong landslide occurred at 16:20 (Beijing time, UTC+8) on the 9th of the December 2018 in Xichuan, China. In this paper, terrestrial radar interferometry used to monitor the Xuyong landslide, GAMMA Portable Radar Interferometer (GPRI), was developed by Gamma Remote Sensing. In this monitoring campaign, the GPRI-II monitoring was carried out five hours and 43 SLC (single-look complex) images were acquired from 2018-12-12 11:30 to 2018-12-12 16:30 (Beijing time, UTC+8).

We use a continuous mode and apply the direct integration method to integrate the 42 interferograms formed by processing each SLC images with the subsequent one. The time-series analysis involves the following steps: 1) Select a reference point located in a stable area. A set of points can be chosen instead of a single point. 2)Calculate 42 interferograms phases relative to the reference point. If a set of reference points are chosen, the last term of the equation is the mean phase computed over the reference points. 3)Integrate phases over time.

The result shows that the displacement at the top of the landslide was very obvious. The maximum measured displacement of the landslide was up to 28mm/d towards the radar during this observation period. The GPIR can observe and recognize the deformation zone in a short time and play an important role in investigating and evaluating landslide stability.

Keywords: Landslide monitoring; GPIR; Time series analysis; Investigate and evaluate

Abstract: Since July 29, 2018, several earthquakes with magnitude 5.5 or above have occurred on Lombok Island, Indonesia, including two earthquakes with magnitude 6.9 on August 5 and 19, and several hundred aftershocks caused by strong earthquake sequences. The continuous occurrence of strong earthquake sequences caused hundreds of deaths, nearly 10,000 people were injured, and thousands of buildings were damaged. As Lombok is a tourist area, the earthquake on July 29 did not cause much damage. The island's tourism plan was still in operation, causing a large number of casualties after the August earthquake. After the occurrence of strong earthquake sequence, many landslides occurred in Rinjani volcano of Lombok Island, and the landform of the whole island changed.

Indonesia is located in the collision area of the Pacific plate, the Indian Ocean plate and the Eurasian plate, sandwiched between the circum-Pacific seismic belt and the Eurasian seismic belt. The crustal activity is intense and the seismicity is frequent in recent years. The Indian Ocean earthquake near Sumatra at the end of 2004 triggered a massive tsunami that killed more than 200,000 people, and the number of deaths in Indonesia alone reached 170,000.

Indonesia is located in the Pacific Volcanic Seismic Zone, and the Indian Ocean near the eastern coast of Indonesia is the junction of three major plate tectonic zones. The three plates are Sunda plate in the east, India plate in the northwest and Australia plate in the southwest. Fractures occur at the concentration of the Indian and Burmese plates. The earthquake in Indonesia occurred further south because the northeastern end of the Australian plate fell below the Sunda plate and, as a result, fell to the lower part of Central Java Province, forming the so-called submerged zone. The downward sliding of the lower plate in the submerged zone usually triggers earthquakes. Experts pointed out that the earthquake in Indonesia was caused by the compression of the two plates in common motion, and the compression of the smaller fault lines behind the submergence lines of the two plates, resulting in the lateral rupture of the plates, which triggered the earthquake.

In this paper, 20 SENTINEL-1A wide-band SAR data are processed by differential interferometry of synthetic aperture radar (D-InSAR), and the co-seismic deformation field of each earthquake in the swarm is obtained. At the same time, Stacking time series analysis and processing were carried out to obtain the results of time series deformation of Lombok Island from the first earthquake in July 2018 to October 2018. The results show that the earthquake swarm caused obvious crustal deformation of Lombok Island in Indonesia, and there are volcanoes in Lombok Island area where the earthquake occurred. The occurrence of strong earthquake swarms destroyed the stability of Rinjani volcano, and landslides occurred continuously. Deformation around the mountain is obvious, with the island falling by 5 to 15 cm, while the surface uplift near the epicenter in the north is about 30 cm. The surrounding areas of Lombok and Rinjani are very unstable. Due to the special location, it is necessary to conduct long-term sequence observations on Lombok and its surrounding islands in order to prevent disasters and reduce disasters.

Keywords: Lombok Island Strong Earthquake Swarm; Rinjani volcano; InSAR; Time Series Analysis

On October 11 and November 3, 2018, two large-scale landslides occurred in Baige Village, Polo Township, Jiangda County, Changdu City, Tibet. The high-speed sliding body rushed into the Jinshajiang River and formed a barrier dam. The barrier lake formed by two sliding failures poses a serious threat to the upstream and downstream areas, which has attracted wide attention. In order to evaluate the hazards of landslides, several questions must be considered: when and where will landslides occur again? How big will the landslide be? How fast and how far do they move? What areas will landslides affect or destroy? How often do landslides occur in a particular area? The answers to these questions require accurate mapping of landslide deformation and prediction of the occurrence of landslides, as well as information on how to avoid or mitigate the impact of landslides. This paper will focus on monitoring the stability of Baige landslide with ground-based radar and provide technical support for subsequent landslide risk assessment. In this paper, the ground-based radar system will be used to obtain the deformation rate and stability of the slope after the landslide occurs. The results show that there is a very large landslide signal on the landslide surface, and the maximum deformation is more than 200 mm/day. At present, the slope is in a relatively stable state, but attention should be paid to the slope stability in rainy season.

As a unpredictable natural disaster, earthquake is common problem in the world. The scenario is a panoramic description of emergencies combined with historical cases and risk simulations. Scenario is different from risk assessment, it is not focus on the local losses, but the risk systems. Through the comprehensive analysis of complex disasters, the corresponding strategy system is formulated. Because the seismic scenario covers almost all complex systems, such as the natural environment, artificial environment, and social environment. Therefore, the large amount of parameter extraction efficiency necessary for seismic scenario simulation is an important factor that restricts the development of the field.

Because remote sensing data has the characteristics of short revisit period, wide field of view, and high data accuracy, this research combines remote sensing and geographic information system (GIS) to simulate an earthquake disaster scenario in Beijing. Firstly, a batch of high-quality remote sensing data of Landsat, which were taken in 1977,1983, 1988, 1993, 1998, 2003, 2008, 2013 and 2017, were selected for change detection, and the age of the buildings in the study area were extracted. Secondly, the historical images of GF2 and GeogleEarth were used to extract building height parameters based on the architectural shadow method, and then the relationship between the age, height and structure of the regional building was established by the survey sample to assess the distribution of building structures in the study area. Thirdly, the construction parameters of the study area were input into the seismic damage factor model to simulate the building damage, and combined with the distribution data of economy, population, lifeline system and key targets by GIS to cross-analyze the seismic impact.

In summary, the combination of remote sensing technology and GIS greatly reduces the extraction efficiency of impact factors for complex disaster systems in large regions, and enables spatial analysis and process simulation of seismic impacts. It can providing clear targets for regional earthquake preparation.

Keywords: Seismic Scenario; Disaster system; Geographic information system (GIS)

On 21 September 1999, the Mw 7.6 Chi-Chi earthquake, one of the largest inland earthquakes in Taiwan happened and struck the Taipei Basin, in the Central western part of the island, killing more than 2400 people and damaging 100 000 structures. The rupture was complex with several dislocations along the 100-km long Chelungpu thrust fault. Revisiting this earthquake with a range of earth observations will allow better understanding of regional fault properties. ERS images from the descending track 232 and covering the period from 21 January 1999 to 28 October 1999 were interferometrically processed using the ESA open-source software SNAP to investigate the co-seismic deformation. With InSAR, only the footwall can be analysed because the hanging-wall, which likely experienced the main deformation in this event, is densely vegetated resulting in low coherence in the interferograms. Co-seismic interferograms show about 10-11 fringes in the footwall which is equivalent to a surface displacement of up to approximately 30 cm. In order to obtain observations of the hanging-wall, Cosi-Corr software was used to correlate pre and post SPOT optical images. In addition to these two datasets, GNSS and leveling data were also used. PSOKINV (Particle Swarm Optimization and Okada Inversion package), a geodetic inversion package, was used to determine the fault geometry and the slip distribution. Firstly, the relative weights of the four datasets were determined using the generalized Akaike’s Bayesian Information Criterion (gABIC). Secondly, the Particle Swarm Optimization (PSO) was utilised in the geodetic modelling to determine an optimal uniform model with 4 fault segments. Thirdly, a joint inversion of InSAR and geodetic data (SPOT, GNSS and leveling) was realised to estimate the slip distribution. These datasets enabled us to get information about the hanging-wall of the fault and to improve the modelling.

Modern SAR technology offers various approaches for processing stacks of interferometric SAR data. For surface motion estimations in urban areas, normally short wavelength data, like X- or C-band, is preferred. For applications in urban areas and infrastructure monitoring. Long wavelength data offers a certain amount of penetration capability and they are less sensitive to temporal decorrelation.

PS-InSAR is a widely used method for surface motion estimation from interferometric SAR data stacks. It is used in commercial applications and also in many projects in the Dragon program, starting from Dragon-1 until today. We consider it a stable technique, proven to successfully and reliably offer surface motion estimations in numerous projects. We used PS-InSAR in Shanghai and Wuhan for estimating urban subsidence and infrastructure stability. With the availability of Sentinel-1 and the large global SAR archive, it is nowadays possible to process PS-InSAR and estimate subsidence in regions of interest all over the world, opening this field up to the public even further.

SAR tomography with long wavelength SAR data, preferably with P-band data, allows foliage penetration and the true 3D reconstruction of the SAR signal under the foliage. This can be used for various applications, e.g. for the estimation of above ground forest biomass. SAR tomography here allows to measure the biomass, instead of estimating it based on tree canopy heights, on a global level. ESA will use this with the upcoming BIOMASS mission.

There are still several problems to be solved though. On problem is the temporal decorrelation. Although P-band is less sensitive to temporal decorrelation, it is still not immune to it. Especially changes in rainfall and canopy water content / water content layers, can cause problems in the 3D reconstruction. With one of the main areas of interest along the tropical rainforest, rain and changes in the rainfall patterns are to be expected though. Minimizing the amount of data necessary for a tomographic inversion is therefore important to allow a good biomass estimation with few acquisitions.

In Dragon-4 we are working closely together towards these goals, continuing our research on PS-InSAR and related techniques, but also extending towards the 3D reconstruction using SAR tomography with long wavelength SAR data.

In recent years, advanced techniques such as polarimetric SAR interferometry (Pol-InSAR) and SAR tomography (TomoSAR) have been widely used to retrieve forest parameters by means of SAR measurements. Pol-InSAR was developed based on the Random Volume over Ground (RVoG) model, which assumes a penetrable volume layer consisting of randomly oriented particles over an underlying rough surface. On this basis, Cloude and Papathanassiou proposed a parametric inversion scheme to retrieve forest height, which has been successfully applied for a variety of forest sites at different frequency bands.

SAR tomography is instead an imaging technique based on the collection of multiple flight lines. It allows focusing the received signal not only in the range/azimuth plane, as in conventional 2-D SAR imaging, but also in elevation, hence providing 3-D resolution capabilities. The retrieval of canopy height using SAR tomography has been considered since the early experiments. Indeed, wave scattering from forested areas is bound to occur between the terrain and the top of the canopy. Hence, canopy height can be retrieved, at least in principle, by tracing the upper envelope in tomographic sections.

In this paper, we aim at presenting an experimental assessment of vegetation height retrieval in tropical forests based on P-band SAR acquisitions. Two approaches are considered: i) parametric height estimation under the assumption of the Random Volume over Ground (RVoG) model, and ii) thresholding the vertical backscattering profiles that are focused by SAR tomography. The data-set under analysis is from the ESA AfriSAR campaign that was flown over Gabon in 2016. Results show that both of the two approaches are able to retrieve forest height to within an accuracy of about 3 m or better over the interval of forest height between 30 m to 50 m at a resolution of 25 m × 25 m

Several upcoming SAR satellite constellations, are going to be operated in bi-static mode, like TanDEM-L or TwinSAR-L, or may have a bi-static companion, like Sentinel-CS. Until now, bi-static data is mainly used for DSM generation, as in the TanDEM mission. In the future, the goal is to use such data also for surface motion estimation. However, current multi-baseline D-InSAR approaches are not well suited for processing this data and need to be adjusted.

The main advantage of a bi-static operation is the minimization of the temporal decorrelation and the atmospheric influence. But, a temporal difference close to zero between the acquisitions also means that ground deformations cannot be measured. Motion related phase components will only appear with a significant time difference between the acquisitions. By acquiring several image pairs over the same area, bi-static missions can deliver such repeat-pass acquisitions with a required temporal baseline, but these interferograms will again suffer from temporal decorrelation and atmospheric effects like the standard acquisitions.

In terms of PSInSAR or related processing methods, that is to say that we would expect an improved estimation of PS point height, but not necessary a better estimation of the deformation phase components, as the most severe problems still occur.

Even more, standard processing chains for PSInSAR will not work well, or at all, with such stacks. In our experiments, we used pursuit mono-static data from the TanDEM-X science phase. The along track baseline is extended to 10 seconds between the satellites, allowing both satellites to transmit and receive data undisturbed from each other. The data is therefore not bi-static and generally suitable for standard InSAR and PSInSAR processing. However, the very small temporal baseline of 10 seconds compared to the 11 days repeat-pass baseline can cause numerical problems in the estimation of the deformation phase.

To avoid this, we separated the estimation of the topographic phase from the estimation of the deformation phase component and use different image pairs in both cases. We estimate the topographic phase only from the 10s pairs. Based on the estimated heights from this first step, we process the deformation phase using the repeat-pass images. Having two images per time can reduce the noise, however we found no significant difference in the performance from this. In areas with high skyscrapers, like our testing area in Guangzhou, China, the deformation estimation can vastly benefit from the much better height estimation of this approach. However, unfortunately, the amount of data available is currently very limited, so that we can only present preliminary results for deformation estimation, showing only slight improvements in this regard.

In synthetic aperture radar (SAR) remote sensing, Differential SAR Tomography (Diff-Tomo) is developing as a powerful crossing of the mature Differential SAR Interferometry and the emerged 3D SAR Tomography, producing advanced 4D (3D+Time) SAR imaging capabilities extensively applied to urban deformation monitoring.

More recently, it has been shown that through Diff-Tomo, identifying temporal spectra of multiple height-distributed decorrelating (forest) scatterers, the important decorrelation-robust forest Tomography functionality is obtained.

To loosen application constraints of the related main experimented full model-based processing, and develop other functionalities, this work presents an advanced adaptive, just semi-parametric, generalized-Capon Diff-Tomo method conceived and developed at University of Pisa (UniPi) for extraction of height and dynamical information of natural distributed (volumetric) scatterers. In addition to robust Tomography, particular reference is to separation of decorrelation mechanisms in forest layers.

Simulated and P-band results are shown. A review of other advanced Diff-Tomo tools developed at UniPi for information extraction in decorrelating forest scenarios is also presented.

Ack.: the Authors thanks Dr. Francesco Cai, formerly at UniPi and now with Leonardo Company, for his support in the SW development.

Reigber, A., Moreira, A.: ‘First demonstration of airborne SAR tomography using multibaseline L-band data,’ IEEE Trans. Geosci. Remote Sens., 2000, 38, (5), pp. 2142-2152

Pardini, M., Papathanassiou, K.: ‘On the estimation of ground and volume polarimetric covariances in forest scenarios with SAR tomography,’ IEEE Geosci. Remote Sens. Lett., 2017, 14, (10), pp. 1860-1864

Huang, Y., Ferro-Famil, L., Reigber, A.: ‘Under-foliage object imaging using SAR tomography and polarimetric spectral estimators,’ IEEE Trans. Geosci. Remote Sens., 2012, 50, (6), pp. 2213-2225

Azcueta, M., Tebaldini, S.: ‘Non-cooperative bistatic SAR clock drift compensation for tomographic acquisitions,’ Remote Sensing, 2017, 9, (11), pp. 1-11

Lombardini, F.: ‘Differential tomography: a new framework for SAR interferometry,’ IEEE Trans. Geosci. Remote Sens., 2005, 43, (1), pp. 37-44

Lombardini, F., Cai, F.: ‘Temporal decorrelation-robust SAR tomography,’ IEEE Trans. Geosci. Remote Sens., 2014, 52,(9), pp.5412-5421

In this paper, the GPUs are used to accelerate the processing efficiencies in time domain (TD) SAR simulation and time domain back-projection (TDBP) focusing. The raw data simulation and back-projection reconstruction are both implemented in the time domain for handling the scenarios of highly non-linear trajectories. The processing inefficiencies, however prevent extensive applications of TD SAR simulation and TDBP focusing. Thus, we utilize the massive parallelism of GPUs to enhance the processing efficiencies. In this contribution, we develop an optimized time-domain SAR simulation algorithm with complexity O(n³). We also discuss the drawback of the optimized simulation method and our contributions to mitigate this problem. Furthermore, both parallel simulation and back-projection focusing algorithms are fully optimized under the NVIDIA’s Compute Unified Device Architecture (CUDA) framework to guarantee a relevant acceleration compared with CPU counterparts. As a result, the GPU-based TD SAR simulation gains a 78x speed-up factor over the CPU serial version. The GPU based TDBP implementation achieve an over 100x speed-up factor compared with the CPU counterpart. To demonstrating the validity of our methods, we apply our GPU based TDBP focusing methods in simulated SAR raw data from highly deviated trajectory and circular trajectory.

Forest above ground biomass (AGB) retrieval by P-band Synthetic Aperture Radar (SAR) tomography has been extensively studied in recent years in the context of the forthcoming spaceborne mission BIOMASS. Most studies made use of airborne data collected in a single day, for which temporal decorrelation could be neglected. This fortunate situation will clearly not be repeatable in the case of BIOMASS, for which the revisit time will be of 3 days. The impact of temporal decorrelation on tomographic observables was analyzed in previous studies using data from the ground-based experiment TropiSCAT, which provided continuous tomographic observations at the expense of covering a small area and providing no azimuth resolution. This paper is meant to complement those studies by investigating the effect of temporal decorrelation on forest canopies over large areas, based on the airborne data-set acquired by DLR during the AfriSAR campaign. The analysis is carried out based on the recently proposed ground-notching technique, which is used to single out volume scattering based on single-baseline acquisitions gathered at a time lag of 4, 5, and 9 days. Results show that volume temporal coherence is consistently between 0.6 and 0.85 when forest height is larger than about 25 m, whereas low vegetation areas appear to be significantly more affected by temporal decorrelation. As a result, the intensity of volume-only scattering is observed to vary to vary by less than 1 dB when ground notching is performed using acquisitions from different dates.

The world s population density in flood-prone coastal zones and megacities is expected to grow up to 25% by 2050. Global sea-levels have risen during the 20th century, and they will rise by up to ~60 cm by 2100. Non-climate-related anthropogenic processes (such as ground subsidence due to groundwater extraction, ground settlements due to large scale land-reclamation, and fast and non-linear subsidence phenomena of artificial sea wall), as well as frequently encountered natural hazards (such as storms and storm-surge) will exacerbate the risk to coastal zones and megacities and amplify local vulnerability. Making the situation worse is the combination of sea-level rise resulting from climate change, local sinking of land resulting from anthropogenic and natural hazards. The coastal vulnerability of Yangtze River Delta (YRD) and Pearl River Delta (PRD) is currently being amplified by the compounding effects of the time-dependent ground subsidence, the accelerated rate of sea level rise, and natural hazards. The provided examples of delta regions affected by the combination of sea-level rise, significant modifications over time, and natural hazards make clear the need of extended analyses for the understanding of the mechanisms at the base of the surface modifications of coastal areas, estimating of future regional sea level change, and evaluating the potential submerged land area [1]-[3].

In this project, the use of well-established remote sensing technologies, based on the joint exploitation of multi-spectral information gathered at different spectral wavelengths, the advanced Differential Interferometric Synthetic Aperture (DInSAR) techniques [4]-[5], GPS/leveling campaigns aiming to perform sound and extended geophysical analyses, satellite altimeter data and tide gauge data, and the Coupled Model Inter-comparison Project Phase 5 (CMIP5) climate model projections are being employed for these purposes. The results obtained in this project represent an asset for the planning of present and future scientific activities devoted to the monitoring of such fragile environments. These analyses are essential to assess the factors that will continue to amplify the vulnerability of the low-elevation coastal zones. The main goals of the project are to provide a full characterization of the scene modifications over time and causes of the coastal delta region environments, to provide estimates of future regional sea level change, to derive coastal submerged area and wave field, and to provide suggestions for implementing coastal protection measures to adapt and mitigate the multi-factors induced coastal vulnerability.

The main achievements obtained during the years of the project will be summarized and discussed at the forthcoming D-4 conference, highlighting the scientific relevance and the expected added value of the project, itself.

References

[1] Yang S. L., Belkin I. M., Belkina A. I., Zhao Q. Y., Zhu J., Ding P. X. (2003) Delta response to decline in sediment supply from theYangtze River: evidence of the recent four decadesand expectations for the next half-century, Estuarine, Coastal and Shelf Sciences, 57, 689-699.

[2] Wang W., Liu H., Li Y., Su J. (2014) Development and managment of land reclamation in China, Ocean & Coastal Management, 102, 415-425.

[3] Zuo J, et al. 2013. Prediction of China’s submerged coastal areas by sea level rise due to climate change. Journal of Ocean University of China, 12(3): 327–334.

[4] Berardino P., Fornaro G.,Lanari R.,Sansosti E.(2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,IEEETransaction on Geoscience and Remote Sensing, 40, 11, 2375-2383.

[5] Zhao Q., Pepe A., Gao W., LuZ., Bonano M., He M.L., Wang J., Tang X. (2015) A DInSAR Investigation of the ground settlement time evolution of ocean-reclaimed lands in Shanghai, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 1763-1781

Shanghai is located at the midpoint of the north–south coastline of China. In order to solve the problem of land scarcity, several reclamation and siltation promotion projects have been implemented since 1995. Due to land reclamation, ground settlement as an inherent problem has arisen in the new lands area, which is responsible for serious damage to infrastructures.

Spaceborne Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an investigating technique capable of extracting line of sight (LOS) cumulative ground settlement measurements with millimeter or even sub-millimeter accuracy. However, the original deformation time-series is produced by single dataset with the using of MT-InSAR technique. Recent years, massive and different types of SAR data are available with the continuous launching of Synthetic Aperture Radar (SAR) satellites. Lei Yu and other scholars have combined 3 platforms’ deformation time-series to retrieve long-term displacement time-series in the ocean-reclaimed areas of Shanghai. In this study, five deformation time-series as well as deformation rates are derived by 5 independent SAR datasets respectively with the using of Small BAseline Subset (SBAS) algorithm. Then, we not only combined 3 platforms’ deformation time-series, we also combined 4 platforms’ deformation time-series. And the combinations were compared by us.

Specifically, five independent SAR datasets are used for this study. The first dataset consists of 35 images, collected by ENVISAT/ASAR(ENV) sensor operated at C band (Ascending, VV polarization) from February 2007 to September 2010. The second dataset consists of 11 images, collected by TerraSAR-X sensor operated at X band (TSX1, Ascending, HH polarization) from December 2009 to December 2010. The third dataset is also collected by TerraSAR-X (TSX2, Descending, VV polarization) from April 2013 to July 2015, consists of 11 images. The fourth dataset consists of 61 images, collected by COSMO-SkyMed(CSK) sensor operated at X band (Descending, HH polarization) from December 2013 to March 2016. The last dataset consist of 33 images, collected by Sentinel-1A(S1A) sensor operated at C band (Ascending, VV polarization) from February 2015 to April 2017. At the beginning, interferometric process is implemented in each dataset separately, and 91, 36, 66, 155, 368 better interference image pairs are sequentially selected. After removing the elevation phase by using ASTER elevation data (30m*30m), it is unwrapped by the Delaunay Minimum Cost Flow(MCF) method. Then, the time coherence coefficient is set to be greater than 0.65 for ENV and CSK, and the other three datasets are set to be greater than 0.55. After that, the deformation time-series and deformation rates of 5 time periods are obtained.

Since TSX1 and TSX2 do not have the overlapping area and they share common areas with other three SAR datasets respectively, we combined deformation time-series of time-overlapped datasets by using Singular Value Decomposition (SVD) method and combined non-time-overlapped datasets by using time-dependent geotechnical models. Three joint strategies, ENV+CSK+S1A, ENV+TSX1+CSK+S1A and ENV+TSX2+CSK+S1A, are implemented respectively. By analyzing the feature points, we found that the annual deformation rate difference between the three joint methods is less than 1mm/y in the area with small settlement. In the areas with obvious subsidence, such as the fourth and fifth runway of Pudong Airport, the annual deformation rate of the three combination fluctuate by ±2.5 mm/y. In terms of deformation time-series, all three combinations have consistent settlement trends.

Over the last decades, the use of Differential Synthetic Aperture Radar Interferometry (DInSAR) [1] technology has gained an increasing attention for its capability to investigate large-scale Earth’s surface deformation phenomena. The DInSAR technique allows the timely monitoring of displacement phenomena with dense grids of measurement points. The availability of measurements over dense spatial grids represents the typifying factor of the DInSAR technology with respect to other conventional approaches (e.g. GPS and levelling measurement campaigns), thus making nowadays DInSAR largely adopted both in scientific and operational frameworks. However, in regions where the density of coherent points is large, the use of dense grid of measurement points leads DInSAR being not very efficient from the computational point of view. Many solutions have been proposed to overcome such a problem. A role of particular importance is covered by the multiresolution/multi-grid [2] algorithms that not only improve computational efficiency but also allow performing a more comprehensive analysis of the deformation phenomena that characterize Earth’s
surface, which exhibit different characteristics at multiple scales of resolution [3].
Recently, DInSAR techniques have largely been used to analyze subsidence phenomena in coastal delta region like Yangtze and the Pearl River Deltas where man-made lands, reclaimed from the sea, are used to build airports, harbors, and industrial areas. In such a context, advanced DInSAR approaches, such as the Persistent Scatterer Interferometry (PSI) [4] and the Small BAseline Subset (SBAS) technique [5], [6], [7] , allow to produce spatially dense velocity maps as well as long-term displacement time-series (with millimeter accuracy even sub-millimeter) corresponding to coherent targets location.

In this study, we develop and discuss the potential of an adaptive quadtree-based decomposition method [8] applied to DInSAR data, which allows one to produce DInSAR deformation products at different scales of resolutions. The latter are adaptively chosen within the imaged scene to better analyze the on-going deformation signals. Specifically, the multi-grid algorithm exploits a multiresolution scheme for the phase unwrapping of sequences of DInSAR interferograms, and shares some similarities with [9]. The selection of the used multi-grids is based on the analysis of the statistical properties of a sequence of interferometric phase that allow to recognize major deformation areas where phase unwrapping operations can be performed more efficiently with a computational improvement and without losing significant information. The algorithm preserves details of deformation as much as possible, and achieves efficient data reduction. The area of interest analyzed is the Pearl River Delta (PRD) region, in particular the island of Hong Kong, which is characterized by subsidence phenomena. Pearl River Delta (PRD) is located on the southern coast of mainland China. It is the third largest delta in China and adjacent to the South China Sea from the north. In the past 50 years, reclaimed lands were merged into just over 100 enclosures protected by flood defenses. However, the coastal area has always been under threat from natural hazards, including river flood, waterlog, typhoon, and tidal flood. These hazards will no doubt be intensified by the predicted sea level rise.

The analysis relies on a set of 60 SAR data acquired by the Sentinel 1A/B radar sensor from December 2017 to January 2019. Starting from these data, we generated a stack of interferograms on which we have tested the new adaptive quadtree decomposition method. The goal of this present investigation is to prove that, at least in correspondence to the highly coherent targets on the ground, the deformation signals can be detected at different scales of resolutions using local, adaptive multilook factors (e.g., 2 x 10, 20 x 4, 40 x 8 and 80 x 16). The proposed method can be integrated with adaptive multi-looking noise filtering techniques [10], [11] to improve accuracy of estimated deformation. The preliminarily results will be presented and discussed at the next Dragon-IV meeting.

[1] D. Massonnet and K. L. Feigl, "Radar Interferometry and its application to changes in the earth's surface," Rev. Geophys., vol. 36, pp. 441-500, 1998.

[2] M. D. Pritt, "Phase Unwrapping by Means of Multigrid Techniques for Interferometric SAR," IEEE Transaction on Geoscience and Remote Sensing, vol. 34, no. 3, pp. 728-738, 1996.

[3] T. Kobayashi, Y. Morishita, H. Yarai and S. Fujiwara, "InSAR-derived Crustal Deformation and Reverse Fault Motion of the 2017 Iran-Iraq Earthquake in the Northwestern Part of the Zagros Orogenic Belt," Geospatial Information Authoriti of Japan, vol. 66, 2018.

[4] A. Ferretti, C. Prati and F. Rocca, "Permanent scatterers in SAR interferometry," IEEE Trans.Geoscience, vol. 39(1), pp. 8-20, 2001.

[5] Q. Zhao , A. Pepe, W. Gao, Z. Lu, M. Bonano, M. He, J. Wang and X. Tang , "A DInSAR investigation of the ground settlement time evolution of ocean-reclaimed lands in Shangai," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp. 1763-1781, 2015.

[6] R. Lanari, O. Mora , M. Manuta , J. J. Mallorqui, P. Berardino and E. Sansosti , "A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms," IEEE Transaction on Geoscience and Remote Sensing, no. 7, pp. 1377-1386, 2004.

[7] F. Falabella, A. Pepe, Q. Zhao, M. Guanyu, C. Serio and R. Lanari, "A hybrid multi-scale InSAR approach to study the 2014-2018 Surface Deformation of the Shanghai Coastal Region through Sequences of Time-Gapped Cosmo-SkyMed SAR acquisitions," Proceedings of Dragon 4 Programme Symposium, 19-22 June 2018, Xi'an, P.R. China.

[8] R. B. Lohman and M. Simons, "Some thoughts on the use of InSAR data to constrain models of surface deformation: Noise structure and data downsampling," Geochem. Geophy. Geosy., vol. 6, no. 1, pp. Q01007-1-Q01007-12, 2005.

[9] C. Wang, X. Ding, Q. Li and M. Jiang , "Equation - Based InSAR Data Quadtree Downsampling for Earthquake Slip Distribution Inversion," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 12, pp. 2060-2064, 2014.

[10] P. Mastro and A. Pepe, "Adaptive Spatial Multi-looking of Differential SAR Interferograms Sequences using Circular Statistic," VDE, pp. 1-6, 2018.

[11] A. Ferretti et al., "A new algorithm for processing interferometric datastacks: SqueeSAR," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 9, p. 3460–3470, 2011.

Shanghai is situated at the mouth of Yangtze River, on the coast of the East China Sea. It is one of the 47 megacities in the world with a population of more than 10 million and is also the financial and science and technology center of China (Dsikowitzky et al. 2016; Kuang et al. 2014). Due to the explosive expanding of the population and economic, Shanghai has started large-scale exploitation of groundwater and infrastructure since the last century which leads to significant land subsidence. According to the records, the cumulative subsidence in the downtown area of Shanghai has exceeded two meters since have leveling data by the 1920s and land subsidence has become one of the most serious urban risks (Zhang et al. 2002). Although groundwater exploitation has been effectively controlled in recent years, the city is still suffering serious risks in view of global sea level rise and the continuous subsidence caused by large-scale infrastructure construction and land reclamation.

During the last century, the subsidence monitoring in Shanghai was mainly by leveling or GPS which was based on single-point survey and will produce large amount of cost. Fortunately, the development of InSAR technology in recent decades has made large-scale, high-frequency, low-cost urban deformation observation possible (Burgmann et al. 2000; Massonnet and Feigl 1998). Especially, MT-InSAR technology, can achieve the ground deformation monitoring with accuracy of millimeter-level, which can meet the needs of high accuracy urban deformation monitoring practice (Lanari et al. 2007; Solari et al. 2016).

Disaster risk assessments is an effective means to qualitatively describe the degree of disaster impact. There have been a large number of related studies in the fields of geology, urban flood risk, and drought, such as the geological risk assessment by fuzzy cluster-analysis methods(FCM) and the flood risk grading based on risk matrix (Efendiyev et al. 2016; Klein et al. 2013). Although MT-InSAR method have been effectively used for studying the ground subsidence in Shanghai in recent decades, these studies are mainly concentrate on the quantitative study of the surface deformation, which are lack of further grading the hazard risk with auxiliary data, such as economic losses, infrastructure vulnerability and land use/land cover data.

In this work, in order to make a relatively detailed assessment, Shanghai is divided into a regular grid matrix according to the size of 100m*100m. And we quantitative scoring each grid with the comprehensive risk assessment matrix, which was consists of deformation time series obtained by Small Baseline Subset (SBAS) algorithm and Sentinel-1A datasets acquired from 2016 to 2018, the land use/land cover data obtained by Landsat-8 images, and the major infrastructure data includes main buildings, flood control levees, and road networks of Shanghai. Subsequently, all of the grids are divided into three levels, including low risk, medium risk and high risk, with support vector machine. Finally, the land subsidence risk rating map of Shanghai was acquired, which will provide a useful reference for the urban risks assessment and the comprehensive management of relevant departments.

[1]Burgmann, R., Rosen, P.A., & Fielding, E.J. (2000). Synthetic aperture radar interferometry to measure Earth's surface topography and its deformation. Annual Review of Earth and Planetary Sciences, 28, 169-209

[2]Dsikowitzky, L., Ferse, S., Schwarzbauer, J., Vogt, T.S., & Irianto, H.E. (2016). Impacts of megacities on tropical coastal ecosystems The case of Jakarta, Indonesia. Marine Pollution Bulletin, 110, 621-623

[3]Efendiyev, G.M., Mammadov, P.Z., Piriverdiyev, I.A., & Mammadov, V.N. (2016). Clustering of Geological Objects Using FCM-algorithm and Evaluation of the Rate of Lost Circulation. Procedia Computer Science, 102, 159-162

[4]Klein, J., Jarva, J., Frank-Kamenetsky, D., & Bogatyrev, I. (2013). Integrated geological risk mapping: a qualitative methodology applied in St. Petersburg, Russia. Environmental Earth Sciences, 70, 1629-1645

[5]Kuang, W., Chi, W., Lu, D., & Dou, Y. (2014). A comparative analysis of megacity expansions in China and the U.S.: Patterns, rates and driving forces. Landscape and Urban Planning, 132, 121-135

[6]Lanari, R., Casu, F., Manzo, M., Zeni, G., Berardino, P., Manunta, M., & Pepe, A. (2007). An overview of the small BAseline subset algorithm: A DInSAR technique for surface deformation analysis. Pure and Applied Geophysics, 164, 637-661

[7]Massonnet, D., & Feigl, K.L. (1998). Radar interferometry and its application to changes in the earth's surface. Reviews of Geophysics, 36, 441-500

[8]Solari, L., Ciampalini, A., Raspini, F., Bianchini, S., & Moretti, S. (2016). PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization. Remote Sensing, 8

[9]Zhang, W., Duan, Z., Zeng, Z., & Kang, Y. (2002). Feature of Shanghai Land Subsidence and Its Damage to Social-economic System. Journal of Tongji University, 30, 1129-1133,1151

China has been affected by some of the world's most serious geological disasters and experiences high economic damage every year. Geohazards occur on remote and highly populated areas.

In the framework of the DRAGON4 32365 Project, this paper presents the main results and conclusions derived from an extensive exploitation of available remote sensing data and methods that allow the evaluation of their importance for various geohazards. Therefore, the great benefits of recent remote sensing data (wide spatial and temporal coverage) that allow a detailed reconstruction of past events and to monitor currently occurring phenomena are exploited to study various areas and various geohazard problems, including: surface deformation of the mountain slopes and glaciers; identification and monitoring of ground movements mining areas and; subsidence, landslides, ground fissure and building inclination studies. Suspicious movements detected in the different study areas were verified and validated by field investigation and measurements in the local.

In the framework of the DRAGON-4 Project, the National Institute of Geophysics and Volcanology of Rome (INGV, Italy) and the Northeastern University of Shenyang (China) collaborate to study the surface movement over industrial regions in Northeast China. The traditional heavy industrial base, especially in the Benxi-Anshan-Shenyang-Fushun (BASF) region, is playing an important role in the economic development of the region, although severe consequences on the local environment are taking place due to the continuous mining activities. Various geo-hazards, such as subsidence, landslides, ground breakage and building inclinations, have been occurring for decades. The continuous monitoring of the effects of the mentioned phenomena is thus of great importance for the safety of the local population. Taking advantage of the availability of dense remote sensing dataset it is possible to analyze the geo-hazards and their environmental impacts in the region; and then making forecast about their occurrence in the future and providing support for disaster prevention and damage reduction.

We adopt multi-temporal InSAR methodologies able to estimate the spatial and temporal deformation over large areas. In this study, we use time-series InSAR results from multiple stacks (from ascending and descending orbits) and different sensors to monitor gravitational deformations and subsidence phenomena in urban areas especially effecting underground paths and railways, and in mining regions.

We take advantages from both the Persistent Scatterers Interferometry (PSI) and Small Baseline Subset (SBAS) techniques, for processing SAR data stacks acquired by Sentinel-1A/B (C-Band) and COSMO-SkyMed (X-Band) exploited for several areas in the BASF region.

In comparison to the 2018 already presented mid-term project results, we investigated the same areas of Fushun and Shenyang analyzing Sentinel-1 datasets, provided by ESA, along both the tracks. The considered time interval spans from June 2015 to December 2018 for the descending orbit, and from April 2017 to September 2018 for the ascending case, respectively.

The retrieved mean ground velocity maps confirm the results from the previous exploited CSK data. Subsidence phenomena are still ongoing reaching values higher than -120 mm/yr inside the mine and -60 mm/yr at its edges. Some small areas (i.e. localized groups of pixels) show positive values (uplift) probably due to stockpile of excavation debris and/or processing waste material.

Two descending stacks of COSMO-SkyMed stripmap images and a stack of TerraSAR-X images covering Shenyang city are exploited in a small baselines subset analysis (SBAS) in our recent study. During the processing of COSMO-SkyMed images，a stack of 18 images acquired from March 13^th 2016 to April 17^th 2017 covering eastern Shenyang and a stack of 15 images acquired from March 1^st 2016 to April 21^st 2017 covering western Shenyang are processed respectively. 58 interferograms are generated out of 18 SAR images for the eastern stack, whereas 44 interferograms are generated out of 15 SAR images for the western stack. In the meantime, 68 interferograms are generated out of 20 TerraSAR-X images acquired from April 15^th 2015 to October 5^th 2016 for SBAS analysis. The topographic phase is simulated and removed from the interferograms using the TanDEM-X DEM of 3-arc-second resolution(with spatial sampling of 90 m× 90 m) covering the study area. The SBAS approach has been proposed to overcome the limitation of decorrelation with reduced amount of SAR images by making full use of all possible interferograms with small spatial and temporal baselines. In this study, a modified SBAS approach developed in StaMPS to ensure the temporal continuity by connecting separated subsets of interferograms is implemented for data processing. The displacements acquired in line of sight direction is translated to vertical direction based on a simple assumption that no horizontal ground motion occurs for subsidence monitoring applications.

As recommended by the Guidelines of InSAR Monitoring for Geo-hazard of the Chinese InSAR community, areas presenting deformation velocities larger than 5mm/yr in LOS can be categorized as subsidence area. Taking the incidence angle into consideration, vertical deformation rate larger than 5.5 mm/yr suggests subsidence in Shenyang. Generally speaking, most parts of Shenyang are relatively stable. However, there is a large area in Tiexi district showing serious subsidence. According to the geological data in Shenyang, the basal ground in this area is generally composed of sandy soil and fine sand. The permeability of the basal ground in this area is quite strong, and therefore instability and ground subsidence could possibly occur in this area. Subsidence is also detected in Tawan, Yushutai and Xiaonanjie area. They have presented a strong connection to the groundwater funnel in Shenyang.

We also processed a new CSK dataset over the Anshan city along the descending track. Moreover, we updated the processing of CSK image dataset for the western part of the city of Shenyang, thanks some new CSK acquisitions (descending track) provided by the Italian space Agency (ASI),

Our results confirm that the heavy industrial exploitation of mines and water pumping in the BASF region of Northeast China cause clear and strong ground deformation effects of high potential impact on the local infrastructures and population. The use of multiple stacks, from different sensors, of InSAR data allows monitoring such phenomena with an accuracy and temporal sampling not possible earlier.

By now, the use of EO products plays a fundamental role to monitor natural and man-induced hazards and to support Disaster Risk Management providing an important tool for local and national organizations.

Acknowledgments

This work is financially supported in part by the National Natural Science Foundation of China (Grant No. 41601378) and the Fundamental Research Funds for the Central Universities (Grant No. N150103001). The COSMO-SkyMed data is provided by ASI via the ASI-ESA Dragon4 Project ID. 32365_4. The TerraSAR-X data is provided by Airbus Defence and Space.

We present a conceptual framework that integrates data-driven modelling with remote sensing to detect and delineate landslide phenomena. The main objective of the associated MSc thesis is to test and implement the developed methodology within an Alpine study site and the Longnan region (China).

The methodological framework includes (i) an initial screening and collection of available data sets (e.g. on past landslide events, environmental data, satellite products) which can then be used to (ii) explore landslide susceptible terrain using data-driven modelling procedures. In this context, EO-based predictor variables (e.g. SRTM-derivatives, land cover information) as well as available landslide information (landslide inventory) will be included into supervised statistical/machine-learning classification techniques. The resulting spatial information on landslide prone zones allows restricting the main area of interest for the subsequent remote sensing based analysis. More specifically, optical remote sensing data (e.g. change detection based on Sentinel-2) will be tested for their potential to identify and map recent landslide phenomena. The ensuing landslide information is expected to further enhance the knowledge on the spatio-temporal occurrence of recent landslide events and to improve the previously described data-driven landslide susceptibility assessment. Depending on the progress of the previous activities, also the potential of Sentinel-1 data (e.g. SAR Interferometry) may be tested to acquire information on slope deformation. The activities associated to this MSc thesis will start in April 2019 and will be completed within December 2019.

Radar Interferometry (InSAR) can provide measurements of surface displacement from Space, with millimetric accuracy [1, 2]. These measurements are used in natural hazards analyses but also for monitoring anthroprogenic activities. In the last few years, the number of SAR satellites with shorter repeat intervals and higher resolutions make increase significantly SAR data volume. This increase as lead to challenges in terms of manual inspection [3], giving in turn rise to the search of automated ways to process the available data. The point previously described and advances in hardware lead to advances in deep learning, which has already been applied in several areas such as computer vision.

We propose a supervised Deep Learning (DL) approach for multivariate outlier detection in post-processing of multitemporal InSAR (MTI) results. We used a Convolution Neural Network (CNN) to process the data leading to one of the following labels: outliers, inliers or potentially dangerous lower coherence points. The input data were organized in such a way that for each point the model has access to the multivariate features (such as velocity, height, etc.) of the nearest points, as well as its coordinates in a local system (centered on each point).

After training and model evaluation, the accuracy, precision and recall were analyzed (the last two for each label), considering a threshold value of 0.6 applied to the model’s output. Our model achieved a 95% accuracy and a mean value of 89%, respectively in precision and recall.

Our research intends to demonstrate the usefulness of DL to detect deformation patterns in post-processing InSAR data, with the purpose of increasing point densities of Permanent Scatterers (PS) point networks, thus enhancing the reliability of InSAR post-processing data.

References

[1] Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78-89.

[2] Bakon, M., Oliveira, I., Perissin, D., Sousa, J., Papco, J. (2017). A Data Mining Approach for Multivariate Outlier Detection in Postprocessing of Multitemporal InSAR Results. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, NO. 6., 2791-2798.

[3] Anantrasirichai, N., Biggs, J., Albino, F., Hill, P., & Bull, D. R. (2018). Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data. Journal of Geophysical Research: Solid Earth, 123, 6592–6606.

Landslide is a hazard that threaten the people who lives in the mountain area, it comes active especially rainy seasons and causes a large number of casualties every year. The movement of the slope is an indicator of activity of the landslide, it is helpful to capture the precursor of the activity, the monitoring of the movement of the slope is very important. Subsidence is a "slowly vary geological hazard". Because of its lagging response and slow progress, subsidence is in mm and not easy to detect. It has the characteristics of long formation time, wide influence range, difficult prevention and control, and difficult to recover. High-accuracy displacement monitoring can help us obtain improved knowledge on the subsidence and landslides. In this work we will show the capability of up-to-date Advanced Land Observing Satellite-2 (ALOS-2) Synthetic Aperture Radar (SAR), Envisat ASAR On-the-Fly Data, Archived Sentinel-1 Data in monitoring the movement of the landslide and subsidence in China, which can capture the fast and slow movement with different spatial and temporal baseline combination, the results shows that the SAR data has its advantage in monitoring the movement of the landslides and subsidence in mountain and city area.

Abstract

Dagushan Iron Mine is the deepest open-pit iron mine in Asia, with abundantiron ore resources. With continuous open-pit mining activities, the stairs extend to underground step by step, and engineering geological conditions are gradually revealed. The factors affecting slope stability are also changing gradually, e.g., exposure of surface water and groundwater. The lithological structure and composition of the slope body are also changing, as well as the effect of blasting on the orebody during mining process, along with the change of the slope safety and stability. As a huge artificial loose accumulation body, instability of the dump will lead to disasters and major engineering accidents for the mine, which not only affectingproductivity, but also causing huge economic loss. Therefore, in order to ensure the safe operation of the mine, it is necessary to conduct slope stability monitoring with non-contact strategy. This kind of non-contact monitoring doesn’t need to install measurement points on the dangerous slope, and thus no need to worry about sliding problems of the measurement points.

As an effective non-contact deformation monitoring tool, SAR interferometry has good potential in displacement monitoring of mines. With a stack of SAR images, time series InSAR is able to overcome spatial and temporal decorrelation problems, as well as the atmospheric phase artifacts, resulting in high precision deformation estimates[1][2]. Amongst various time series InSARalgorithms, small baseline subsets analysis (SBAS) is able to estimate deformation using all the high quality interferograms, which improves the utilization of SAR data and is suitable for analysis on long time series[3]. Therefore, the SBAS method is used to monitor the displacements in Dagushan open-pit iron mine. In this paper, 117 sentinel-1 images acquired from 2017 to 2019 are used, as well as the 3-arc-second DEM generated by the German TanDEM-X mission[4][5]. With height accuracy of approximately 1m, TanDEM-X DEM can be used to remove the topographic phase from the interferograms.

During data processing, a super master is first selected according to the spatial and temporal baselines. All the slave images are coregistered to the super master image during coarse coregistration and fine coregistration. Then, high quality interferograms with small spatial and temporal baselines are generated following a multi-master strategy. With the high density in time and space, as many interferograms as possible participate in displacement estimation. The short spatial baselines can reduce the influence of DEM error on deformation estimates. In order to improve the quality of interferograms, Goldstein filter is applied on all interferograms. Then, phase unwrapping based on minimum cost flow is conducted for each interferogram.The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out. Based on the unwrapped interferograms, the average displacement rate and displacement time series are estimated using singular value decomposition method. The estimated displacements map in line-of sight direction show that the northern slope, western part and the northern part of the dump suffer from severe displacements. In order to assess the precision of the displacement estimates, a comparison with on-site date collected by measurement robots is carried out. There is a very good consistency between the two results. The outcome of this study can help with mine disaster prevention and mitigation, and provide technical support for ensuring safe mining activities.

Keywords: small baseline subsets analysis, displacement monitoring, open-pit mine

References

[1]Ferretti, A.; Prati, C.; Rocca, F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2202–2212.

[2] Ferretti, A.; Prati, C.; Rocca, F. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sens. 2001, 39, 8–20.

[3] Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience & Remote Sensing, 2003, 40(11):2375-2383.

[4] Torres R ,Snoeij P , Geudtner D , et al. GMES Sentinel-1 mission[J]. Remote Sensing of Environment, 2012, 120(6):9-24.

[5] Huber, M.; Gruber, A.; Wendleder, A.; Wessel, B.; Roth, A.; Schmitt, A. The Global TanDEM-X DEM: Production Status and First Validation Results. In Proceedings of the 2012 XXII ISPRS Congress International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012; Volume XXXIX-B7, pp. 45–50.

Glacier motion represent a significant reference for the hazard assessment of glacier and glacial lakes. GF-3, as the first civil spaceborne synthetic aperture radar satellite in China, has important advantages in monitoring glacier motion due to its characteristics of all-weather, all-time capabilities and high spatial resolution. In this paper, based on five GF-3 images with FSⅡ imaging modes, the surface velocities of the Yiga Glacier, located in Nyenchen Tonglha Mountains, are estimated over five time periods using offset tracking technique during November 2017 to March 2018. The results were compared with the offset tracking results of sentinel-1 images which have a similar time with GF-3 image and based on the assumption that the velocity of the bedrock in the study area should be 0, the velocity residuals of the bedrock in each period are calculated, then the applicability of GF-3 image in monitoring glacier surface motion was evaluated. The results of GF-3 images show that the distribution of Yiga Glacier motion is similar in four periods, and the maximum surface velocities are all distributed in the central part of the glacier where the elevation changes dramatically. Meanwhile, the results are consistent with the results of sentinel-1 based on two images. The RMSEs of velocity residuals in the bedrock area in four periods are 1.4 cm/d, 2.0 cm/d, 1.7 cm/d and 2.3 cm/d, respectively, which validate the reliability of the deformation estimated used GF-3 images in this paper. Based on the above analysis, GF-3 SAR data can be used as one of the conventional data sources for monitoring glacier surface movement. Because of its high spatial resolution and high cost performance, GF-3 can play a unique role in monitoring the motions of glaciers.

Land subsidence is one of the most common environmentalproblems inurban areas around the world ^[1,2]. It has been hindering social stability and sustainable development for a long time.The deformation of the earth's surface and the structures upon it is usually a long-term gradual process. As the economic and cultural center of northeast China, Shenyang is developing rapidly in recent decades.With continuous above-ground and under-ground construction, Shenyang is suffering from continuous subsidence during a long time span.Therefore,continuous subsidence monitoring is essential in Shenyang.

As aspaceborne geodetic technology, synthetic aperture radar Interferometry (InSAR) is widely used in surface topography measurement and deformation monitoring.Using a stack of SAR images, Time Series InSAR is capable of overcoming decorrelation problems and monitoring land subsidence with very high accuracy. Several Time Series InSAR technologies such as Persistent Scatterer SAR Interferometry (PSI) ^[3], Small Baselines Subsets Analysis(SBAS) ^[4],Pixel Offset Tracking (POT) ^[5] and otherInSARtime series analysis algorithms have been widely used to monitor surface deformation.In this paper, three stacks of high-resolution TerraSAR-X and COSMO-SkyMed datasets are used to monitor the ground subsidence of Shenyang by means of SBAS. The COSMO-SkyMed images are acquired in descending orbit, including 15 images covering western Shenyang and 18 images covering eastern Shenyang. Both stacks are acquired during March 2016 and Apirl 2017. The 20 TerraSAR-X images are acquired in ascending orbit from August 2015 to October 2016. Besides, TanDEM-X DEM of 3-arc-second resolution(with spatial sampling of 90 m × 90 m) covering the study area is used to simulate and remove topographic phase from the interferograms ^[6,7].

In this paper, the modified SBAS approach in StaMPS is used for time-series InSAR analysis, due to its ability to ensure temporal continuity by connecting separated subsets of interferograms with larger baselines. Theoretically, a complex multilook operation to mitigate the effects of the decorrelation noise should be independently carried out before generating interferograms^[8].In this study, the spatial resolution of COSMO-SkyMed and TerraSAR-X are similar in size, so we could skip this step.The residual topographic artifacts, as well as the atmospheric phase screen (APS) signals, are also estimated and filtered out^[9-11].Based on an assumption that subsidence only happens in vertical direction, the estimated deformation in Line of Sight (LOS) is translated to vertical displacements.

Targeting at revealing the long-term ground subsidence, a fusion method based on nonlinear curve fitting is implemented using the overlapping time period between the TerraSAR-X and COSMO-SkyMed datasets from March 2016 to October 2016.It is revealed that the synergistic results of COSMO-SkyMed and TerraSAR-X datasets can obtain a more comprehensive understanding of the slow-moving subsidence.The subsidence results in this paper show a very good consistency with geological conditions and ground water funnel distribution in Shenyang City.Generally speaking, most parts of Shenyang are relatively stable. However, there’s a large area in Tiexi district showing serious subsidence. According to the geological data in Shenyang, the basal ground in this area is generally composed of sandy soil and fine sand. The permeability of the basal ground in this area is quite strong, and therefore instability and ground subsidence could possibly happen to this area. Ground subsidence is also detected in Tawan, Yushutai and Xiaonanjie area. They have presented a strong connection to the groundwater funnel in Shenyang.

IndexTerms—Time Series InSAR, Subsidence,SBAS, Fusion

5.REFERENCES

[1]. Pradhan, B.; Abokharima, M.H.; Jebur, M.N.; Tehrany, M.S. Land subsidence susceptibility mapping atKinta Valley (Malaysia) using the evidential belief function model in GIS. Nat. Hazards 2014, 73, 1019–1042.

[2].YusupujiangA , Fumio Y , Wen L . Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan[J]. Remote Sensing, 2018, 10(8):1304-.

[3]Ferretti, APermanent scatterers in SAR interferometry.IEEE Trans Geosci Remote Sens 39(2001).

[4] Berardino, Paolo , et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms. IEEE Transactions on Geoscience and Remote Sensing 40.11(2002):2375-2383.

[5] T., Strozzi , et al. Glacier motion estimation using SAR offset-tracking procedures.Geoscience& Remote Sensing IEEE Transactions on 40.11(2002):2384-2391.

[6] Huber, M.; Gruber, A.; Wendleder, A.; Wessel, B.; Roth, A.; Schmitt, A. The Global TanDEM-X DEM: Production Status and First Validation Results. In Proceedings of the 2012 XXII ISPRS Congress International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, 25 August–1 September 2012; Volume XXXIX-B7, pp. 45–50.

[7] The TanDEM-X 90m Digital Elevation Model. Available online: https://geoservice.dlr.de/web/dataguide/tdm90/ (accessed on 31 October 2018).

[8] Rosen, P. A., et al. Synthetic aperture radar interferometry. Proceedingsofthe IEEE. 88.3(2002):333-382.

[9] Lanari, Riccardo , et al. An Overview of the Small Baseline Subset Algorithm: a DInSAR Technique for Surface Deformation Analysis. Pure and Applied Geophysics164.4(2007):637-661.

[10] Manzo, Mariarosaria , et al. A Quantitative Assessment of DInSAR Measurements of Interseismic Deformation: The Southern San Andreas Fault Case Study.Pure& Applied Geophysics 169.8(2012):1463-1482.

[11] Pepe, A., et al. The study of the deformation time evolution in coastal areas of Shanghai: A joint CX-band SBAS-DInSARanalysis.Geoscience& Remote Sensing Symposium 2015.

[12]Jing,L, The study of Physical and Mechanical Properties of Soil and Engineering Geological In Shenyang City Center.[D]. 2015.

The application of the traditional InSAR time series technology is often limited by the little measure points on the surface of the landslides, especially in the region with dense vegetation. In order to overcome its disadvantages corresponding to the surface characteristics of landslides, the DS-InSAR time series technology was presented and employed in monitoring of Jiaju landslide status. Compared with the SBAS-InSAR technology, the presented DS-InSAR time series approach could yield much more high dense measure points on the surface of landslide. The distributed location and the motion variation of landslide were apparently shown in the final deformation results. Therefore, the DS-InSAR time series approach would be valuable and has great potential in landslide hazard monitoring.

Sentinel-1 (S1) data have the potential to measure, by radar interferometry (InSAR), the present-day Earth surface deformation, whether of tectonic origin or otherwise (anthropogenic, hydrological), on the scale of specific local targets (active faults, sedimentary basins, cities) as well as on the continental scale (large lithospheric blocks bordered by mountain ranges and major fault systems). With now more than 4 years of S1 images archive, the global coverage and high-temporal resolution of these images thus allow to investigate the dynamics of slow aseismic slip on faults, a critical step to better understand physical processes involved in the generation of large earthquakes, in various tectonic contexts worldwide. They also allow a refined quantification of strain partitioning across complex fault systems, as well as of the degree of strain localization on faults, which can be confronted to different fault system evolution scenarios and lithospheric deformation mechanisms. In cases where non-tectonic deformation superimpose with tectonic deformation, time-series analysis helps extracting the specific spatio-temporal signature of each phenomena. We will illustrate these different applications with our most recent case studies, in Asia in particular, based on InSAR time-series analysis of S1 data.

With the medium-resolution (~2.0 meter in azimuth and ~13.0 meter in range for TOPS/IW mode) SAR data, it is possible to acquire large scale deformation (>1000 km) in a continuous TOPS scanning. With the temporal sampling of 6-day or times of it, Sentinel-1 SAR data were quickly accumulated since later of 2014. However, processing of the large data set is a challenge, which is useful and/or a requirement for some typical applications, such as tectonic deformation analysis or anthropogenic activity monitoring for a vast region.

We utilize high-performance computation (HPC) for this purpose, which is widely used for scientific applications. To accelerate processing, we adopt Gamma processor for conventional processing with a benefit of multiple-core parallel processing on each node, and it dramatically reduces the time cost for TOPS mode SAR data alignments. On HPC with multiple nodes, the data alignment and interferometric processing procedures were deployed on each node, without communications between nodes required. After preprocessing with Gamma, multiple doppler-deramped and coregistered images are prepared for time-series analysis. In this stage, we adopt the sophisticated processor StaMPS (Hooper et al., 2007) for PS and SBAS analysis, or combine the two methods for hybrid analysis. Due to patch-level parallelization, the large-scale data could be divided into multiple patches with different dimensions and they are processed on each node simultaneously.

We applied our two-level processing approach in multiple challenge areas, the North China Plain, the Longmenshan area and other Tibet regions for both anthropogenic activity monitoring and tectonic deformation detections. Both areas are quite tricky for normal InSAR processing, but with our HPC parallel system, we acquire consistent results compared with GPS observations. The method conducted in these tests confirmed the robustness of our approach for deformation detection with Sentinel-1 large scale InSAR data.

The North China Plain (NCP) is a vital agricultural region and is highly-populated, so the groundwater utilization is quite heavy in this region for irrigation and human beings. This leads to an overdraw of groundwater and fast subsidence over the whole area.

We utilize high-performance computation (HPC) for detection of the related deformation. To accelerate processing, we adopt Gamma processor for conventional processing with a benefit of multiple-core parallel processing on each node, and it dramatically reduces the time cost for TOPS mode SAR data alignments. On HPC with multiple nodes, the data alignment and interferometric processing procedures were deployed on each node, without communications between nodes required. After preprocessing with Gamma, multiple doppler-deramped and coregistered images are prepared for time-series analysis. In this stage, we adopt the sophisticated processor StaMPS (Hooper et al., 2007) for PS and SBAS analysis, or combine the two methods for hybrid analysis. Due to patch-level parallelization, the large-scale data could be divided into multiple patches with different dimensions and they are processed on each node simultaneously.

Our processing shows that in the center of the NCP, farming activity produces widely distributed deformation, not only localized subsidence bowls as in typical subsidence regions. Our results are also consistent with GPS observations on this scale. Tectonic units in the same region could bound the subsidence behavior, hence both kinds of activities may have some kind of interactions.

The global and systematic coverage of Sentinel-1 radar images enables to characterize, by radar interferometry, surface deformations at the scale of large active faults. This represents considerable progress in fault monitoring and opens new perspectives in seismic hazard assessment. Our study focuses on the Yushu - Ganzi - Xianshuihe active fault system (YGX), located on the eastern part of the Tibetan plateau. This left-lateral fault system accommodates the collision between the Indian and the Eurasian plates. The Ganzi segment may represent a 350 km-long seismic gap, unbroken for the past ~120 years. To measure the interseismic deformation across the YGX fault system, we perform a time series analysis of 4 years of Sentinel-1 InSAR data, acquired along ascending and descending orbits, using the New Small Baseline Subset processing chain including the latest adaptations (Doin et al., 2011, Grandin, 2015). The results are presented as mean velocity maps across the faults and compared to previous GPS studies and the long-term fault history. Simple elastic models of velocity profiles are also derived. They show that the Ganzi gap may be the site of aseismic slow slip which, depending on its spatio-temporal characteristics, could contribute to reduce seismic hazard on the fault or, conversely, facilitate the initiation of future major ruptures. The characterization of strain partitioning and strain localization across this fault system enables to precisely evaluate spatial and temporal variations of slip at various depths on the fault and constitutes a key constraint on seismic hazard assessment and lithospheric deformation mechanisms.

Approaches of monitoring earthquakes have evolved from conventional ground-based networks to include space. In the areas of seismology, geology and geophysics, scientists believe that the events leading up to earthquakes goes through a complex process and the process is somehow chaotic. Understanding earthquakes requires a breakthrough from traditional approaches to utilizing advanced technology. In fact, the seismology discipline has expanded the scope of earthquake study from conventional ground-based observations to space. In particular, since the Swarm satellite mission lunched in 2013, they have paved a way to provide a wide range of measurements in space by Vector Field Magnetometer, Absolute Scalar Magnetometer, Electrical Field Instrument, etc. instrumental sensors.

The measurements delivered by the three satellite are very valuable for a range of applications, including earthquake prediction study. However, for more than 5 years, relatively little advancement has been achieved on establishing a systematic approach for detecting anomalies from the satellite measurements for predicting earthquakes before they occur. This report presents a continuous effort, describing essential functional components of a system for automatic anomaly detection. Through a case study we demonstrate the functionality of the system in detecting anomalies, and the process of data processing and analysis along with experience in developing a viable tool for precisely discovering seismic anomalies from the observed data by the Swarm satellites.

Earthquake is one of the most severe natural disasters. More than half of lives lost is caused by earthquake among natural disasters in the world. The earthquake also causes huge economic losses, e.g., about 800 billion RMB were lost during the Wenchuan EQ (Mw=7.8, 2008). Therefore, the governments and scientist of many countries, especially of those with frequent earthquakes, pay great attention to the study of earthquake prediction. It was in the 1960s that the study on the earthquake prediction started and national projects for EQ prediction designed in several countries e.g., China, Japan, USSR and USA (Chen, et al, 2000, Uyeda, 2015).

As well known, the earthquake prediction is a difficult scientific problem in the world. It is often debated and doubtful about that earthquake could be predicted and about whether there is any observable anomalous precursor prior to the earthquake. During the last decades, a lot of example of observable short-term precursors was published letting more scientists considering that there indeed exists precursor before an earthquake and it can be observed only if the reasonably effective method is used. The electromagnetic (EM) method is believed by scientists to be one of the methods that can be used to first reach success in short-term prediction. In this report, we will introduce the first EM observation network built recently using the alternate EM field and its exploitation for monitoring earthquakes. As an example, we will present how to use natural EM source to capture the precursors before the 5.1 Ms Yangbi earthquake in Yunnan province, China, along with a comparative study with the result detected from the Swarm electromagnetic data in the corresponding period of time.

The study is supported by NSFC (41674081,41374077）and NDICC (15212Z0000001). The members of EM group of Institute of Geology, China Earthquake Administration and colleagues in the Yunnan Earthquake Administration joined the construction of the network and data observation.

Since the Swarm satellite constellation launched November 2013, three satellites have delivered immense amounts of geomagnetic field measurements. Currently it is very extremely difficult for researchers to track observed data based on the instrumental sensors, such as Vector Field Magnetometer (VFM), Absolute Scalar Magnetometer (ASM), and conduct anomaly detection, it is imperative to develop more effective data analytics for detecting and discovering abnormalities in the electromagnetic time series data for earthquake studies.

In this report, we propose a Martingale framework which could be adequate for assessing abnormal changes within electromagnetic data streams. The martingale method becomes essential as traditional statistical approach are inappropriate for the high dimensional electromagnetic dataset (Vapnik, 1998). The first step using the framework is to categorically obtain a practical data model in the machine learning standard scenarios through the use of strangeness measures. The strangeness measures establish a way of testing the exchangeability assumption of the dataset using a hypothesis test which drives the martingale process. The Martingale model will also involve the use of machine learning smoothing techniques to reduce noise and other interference efficiently making the framework more sensitive in detecting change point/anomaly. And finally, the model will be evaluated over Swarm electromagnetic data based on the two selected earthquakes, compared against a benchmark method and studied on its effectiveness in detecting abnormal changes before the earthquakes.