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.

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[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.