A Decade of Ground Deformation in Kunming (China) Revealed by Multi-Temporal Synthetic Aperture Radar Interferometry (InSAR) Technique

Large-scale urbanization has brought about severe ground subsidence in Kunming (China), threatening the stability of urban infrastructure. Mapping of the spatiotemporal variations of ground deformation is urgently needed, along with summarization of the causes of the subsidence over Kunming with the purpose of disaster prevention and mitigation. In this study, for the first time, a multi-temporal interferometric synthetic aperture radar (InSAR) technique with L-band Advanced Land Observation Satellite (ALOS-1) and X-band Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) data was applied to Kunming to derive the time series deformation from 2007 to 2016. The annual deformation velocity revealed two severe subsiding regions in Kunming, with a maximum subsidence of 35 mm/y. The comparison of the deformation between InSAR and leveling showed root-mean-square error (RMSE) values of about 4.5 mm for the L-band and 3.7 mm for the X-band, indicating that our results were reliable. We also found that the L-band illustrated a larger amount of subsidence than the X-band in the tested regions. This difference was mainly caused by the different synthetic aperture radar (SAR)-acquired times and imaging geometries between the L- and X-band SAR images. The vertical time series deformation over two severe subsiding regions presented an approximate linear variation with time, where the cumulative subsidence reached 209 mm during the period of 2007–2016. In view of relevant analyses, we found that the subsidence in Kunming was the result of soft soil consolidation, building load, and groundwater extraction. Our results may provide scientific evidence regarding the sound management of urban construction to mitigate potential damage to infrastructure and the environment.

[1]  Rémi Prébet,et al.  A Data-Adaptive EOF-Based Method for Displacement Signal Retrieval From InSAR Displacement Measurement Time Series for Decorrelating Targets , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Xue Chuan-dong Clay Minerals in Quaternary Clayey Soil and Its Relation to the Land Subsidence in Kunming Basin Area , 2001 .

[3]  Olaf Hellwich,et al.  Towards Global Volcano Monitoring Using Multisensor Sentinel Missions and Artificial Intelligence: The MOUNTS Monitoring System , 2019, Remote. Sens..

[4]  Howard A. Zebker,et al.  Decorrelation in interferometric radar echoes , 1992, IEEE Trans. Geosci. Remote. Sens..

[5]  David M. Potts,et al.  The Influence of Building weight on Tunnelling-Induced Ground and Building Deformation , 2004 .

[6]  Rui Zhang,et al.  Retrieving Three-Dimensional Co-Seismic Deformation of the 2017 Mw7.3 Iraq Earthquake by Multi-Sensor SAR Images , 2018, Remote. Sens..

[7]  Xiaoli Ding,et al.  Pixel-Wise MTInSAR Estimator for Integration of Coherent Point Selection and Unwrapped Phase Vector Recovery , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Zhen-Dong Cui,et al.  Model test study of land subsidence caused by high-rise building group in Shanghai , 2008 .

[9]  Nantheera Anantrasirichai,et al.  A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets , 2019, Remote Sensing of Environment.

[10]  Karsten Schulz,et al.  Generalization of the CoVAmCoh analysis for the interpretation of arbitrary InSAR images , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[11]  R. D. Ramsey,et al.  Land cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests , 2014 .

[12]  Xu Chen,et al.  Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China , 2017 .

[13]  Semih Ergintav,et al.  Analysis of Secular Ground Motions in Istanbul from a Long-Term InSAR Time-Series (1992-2017) , 2018, Remote. Sens..

[14]  Xiaoli Ding,et al.  Two-dimensional deformation monitoring over Qingxu (China) by integrating C-, L- and X-bands SAR images , 2014 .

[15]  Wei Gao,et al.  Generation of long-term InSAR ground displacement time-series through a novel multi-sensor data merging technique: The case study of the Shanghai coastal area , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[16]  Yang Lan,et al.  Optimal Baseline Design for Multibaseline InSAR Phase Unwrapping , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Kazhong Deng,et al.  Monitoring and analysis of mining 3D time-series deformation based on multi-track SAR data , 2018, International Journal of Remote Sensing.

[18]  Yunmeng Cao,et al.  Time-series InSAR ground deformation monitoring: Atmospheric delay modeling and estimating , 2019, Earth-Science Reviews.

[19]  LI Wen-chang,et al.  Geological characteristics of the Pulang porphyry copper deposit, Yunnan , 2006 .

[20]  Fu Jun-qu Research of Cause and Classification of the Shallow Layer Soft Soil for Kunming Basin , 2000 .

[21]  Fabio Rocca,et al.  Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry , 2000, IEEE Trans. Geosci. Remote. Sens..

[22]  L. Thomas,et al.  Land subsidence caused by ground water withdrawal in urban areas , 1985 .

[23]  Mario Costantini,et al.  A novel phase unwrapping method based on network programming , 1998, IEEE Trans. Geosci. Remote. Sens..

[24]  Takahiro Osawa,et al.  Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang, Indonesia, using multi-sensor satellite data , 2018 .

[25]  Wendy Zhou,et al.  Four-dimensional filtering of InSAR persistent scatterers elucidates subsidence induced by tunnel excavation in the Sri Lankan highlands , 2019, Journal of Applied Remote Sensing.

[26]  Ewa Glowacka,et al.  Multi-sensor DInSAR applied to the spatiotemporal evolution analysis of ground surface deformation in Cerro Prieto basin, Baja California, Mexico, for the 1993–2014 period , 2018, Natural Hazards.

[27]  Tsehaie Woldai,et al.  Monitoring dewatering induced subsidence and fault reactivation using interferometric synthetic aperture radar , 2009 .

[28]  Jianping Wu,et al.  Automated derivation of urban building density information using airborne LiDAR data and object-based method , 2010 .

[29]  Xiaoli Ding,et al.  Landslide monitoring by combining of CR-InSAR and GPS techniques , 2014 .

[30]  Libin Chen,et al.  Determination of Optimum Tie Point Interval for SAR Image Coregistration by Decomposing Autocorrelation Coefficient , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Mahdi Motagh,et al.  Ground surface response to continuous compaction of aquifer system in Tehran, Iran: Results from a long-term multi-sensor InSAR analysis , 2019, Remote Sensing of Environment.

[32]  Mike P. Stewart,et al.  A modification to the Goldstein radar interferogram filter , 2003, IEEE Trans. Geosci. Remote. Sens..

[33]  Wen Liu,et al.  Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan , 2018, Remote. Sens..

[34]  Joong-Sun Won,et al.  Detecting the Source Location of Recent Summit Inflation via Three-Dimensional InSAR Observation of Kīlauea Volcano , 2015, Remote. Sens..

[35]  T. Meckel,et al.  Current subsidence rates due to compaction of Holocene sediments in southern Louisiana , 2006 .

[36]  Thomas Fuhrmann,et al.  Resolving Three-Dimensional Surface Motion with InSAR: Constraints from Multi-Geometry Data Fusion , 2019, Remote. Sens..

[37]  Jiajun Chen,et al.  Small Magnitude Co-Seismic Deformation of the 2017 Mw 6.4 Nyingchi Earthquake Revealed by InSAR Measurements with Atmospheric Correction , 2018, Remote. Sens..

[38]  Federico Raspini,et al.  From ERS 1/2 to Sentinel-1: Subsidence Monitoring in Italy in the Last Two Decades , 2018, Front. Earth Sci..

[39]  Luke Bateson,et al.  A Methodology to Detect and Characterize Uplift Phenomena in Urban Areas Using Sentinel-1 Data , 2018, Remote. Sens..

[40]  Zhong Lu,et al.  Research on Spatiotemporal Land Deformation (2012-2018) over Xi'an, China, with Multi-Sensor SAR Datasets , 2019, Remote. Sens..

[41]  Z. Yue,et al.  Review on current status and challenging issues of land subsidence in China , 2004 .

[42]  Hyung-Sup Jung,et al.  A Novel Multitemporal InSAR Model for Joint Estimation of Deformation Rates and Orbital Errors , 2014, IEEE Transactions on Geoscience and Remote Sensing.