Conference Agenda

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