Browse > Article
http://dx.doi.org/10.12989/sss.2020.26.3.277

Ground surface changes detection using interferometric synthetic aperture radar  

Foong, Loke Kok (Department for Management of Science and Technology Development, Ton Duc Thang University)
Jamali, Ali (Faculty of Surveying Engineering, Apadana Institute of Higher Education)
Lyu, Zongjie (Institute of Research and Development, Duy Tan University)
Publication Information
Smart Structures and Systems / v.26, no.3, 2020 , pp. 277-290 More about this Journal
Abstract
Disasters, including earthquakes and landslides, have enormous economic and social losses besides their impact on environmental disruption. Iran, and particularly its Western part, is known as an earthquake susceptible area due to numerous strong ground motions. Studying ecological changes due to climate change can improve the public and expert sector's awareness and response to future disastrous events. Synthetic Aperture Radar (SAR) data and Interferometric Synthetic Aperture Radar (InSAR) technologies are appropriate tools for modeling and surface deformation modeling. This paper proposes an efficient approach to detect ground deformation changes using Sentinel-1A. The focal point of this research is to map the ground surface deformation modeling is presented using InSAR technology over Sarpol-e Zahab on 25th November 2018 as a study case. For surface deformation modeling and detection of the ground movement due to earthquake SARPROZ in MATLAB programming language is used and discussed. Results show that there is a general ground movement due to the Sarpol-e Zahab earthquake between -7 millimeter to +18 millimeter in the study area. This research verified previous researches on the advanced image analysis techniques employed for mapping ground movement, where InSAR provides a reliable tool for assisting engineers and the decision-maker in choosing proper policies in a time of disasters. Based on the result, 574 out of 682 damaged buildings and infrastructures due to the 2017 Sarpol-e Zahab earthquake have moved from -2 to +17 mm due to the 2018 earthquake with a magnitude of 6.3 Richter. Results show that mountainous areas have suffered land subsidence, where urban areas had land uplift.
Keywords
sentinel; SAR; InSAR; forest; disaster monitoring; earthquake;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Fang, L., Xu, Y., Yao, W. and Stilla, U. (2016), "Estimation of glacier surface motion by robust phase correlation and point like features of SAR intensity images", ISPRS J. Photogram. Remote Sensing, 121, 92-112. https://doi.org/10.1016/j.isprsjprs.2016.08.012   DOI
2 Farolfi, G., Piombino, A. and Catani, F. (2019), "Fusion of GNSS and Satellite Radar Interferometry: Determination of 3D Fine-Scale Map of Present-Day Surface Displacements in Italy as Expressions of Geodynamic Processes", Remote Sensing, 11(4), 394. https://doi.org/10.3390/rs11040394   DOI
3 Barnhart, W.D., Brengman, C.M., Li, S. and Peterson, K.E. (2018), "Ramp-flat basement structures of the Zagros Mountains inferred from co-seismic slip and afterslip of the 2017 Mw7.3 Darbandikhan, Iran/Iraq earthquake", Earth Planet. Sci. Lett., 496, 96-107. https://doi.org/10.1016/j.epsl.2018.05.036   DOI
4 Baziar, M.H. and Rostami, H. (2017), "Earthquake demand energy attenuation model for liquefaction potential assessment", Earthq. Spectra, 33(2), 757-780. https://doi.org/10.1193/030816eqs037m   DOI
5 Berardino, P., Fornaro, G., Lanari, R. and Sansosti, E. (2002), "A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms", IEEE Transact. Geosci. Remote Sensing, 40(11), 2375-2383. https://doi.org/10.1109/TGRS.2002.803792   DOI
6 Moayedi, H., Osouli, A., Tien Bui, D. and Foong, L.K. (2019d), "Spatial landslide susceptibility assessment based on novel neural-metaheuristic geographic information system based ensembles", Sensors, 19(21), 4698. https://doi.org/10.3390/s19214698   DOI
7 Moayedi, H., Mehrabi, M., Kalantar, B., Abdullahi Mu'azu, M.A., Rashid, A.S., Foong, L.K. and Nguyen, H. (2019a), "Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial hazard assessment of seismic-induced landslide", Geomat. Natural Hazards Risk, 10(1), 1879-1911. https://doi.org/10.1080/19475705.2019.1650126   DOI
8 Moayedi, H., Mehrabi, M., Mosallanezhad, M., Rashid, A.S.A. and Pradhan, B. (2019b), "Modification of landslide susceptibility mapping using optimized PSO-ANN technique", Eng. Comput., 35(3), 967-984. https://doi.org/10.1007/s00366-018-0644-0   DOI
9 Moayedi, H., Osouli, A., Bui, D.T., Kok Foong, L., Nguyen, H. and Kalantar, B. (2019c), "Two novel neural-evolutionary predictive techniques of dragonfly algorithm (DA) and biogeography-based optimization (BBO) for landslide susceptibility analysis", Geomat. Natural Hazards Risk, 10(1), 2429-2453. https://doi.org/10.1080/19475705.2019.1699608   DOI
10 Moayedi, H., Tien Bui, D. and Kok Foong, L. (2019e), "Slope stability monitoring using novel remote sensing based fuzzy logic", Sensors, 19(21), 4636. https://doi.org/10.3390/s19214636   DOI
11 Montecino, H.D., de Freitas, S.R., Baez, J.C. and Ferreira, V.G. (2017), "Effects on Chilean Vertical Reference Frame due to the Maule Earthquake co-seismic and post-seismic effects", J. Geodyn., 112, 22-30. https://doi.org/10.1016/j.jog.2017.07.006   DOI
12 Motaghi, K., Shabanian, E. and Kalvandi, F. (2017), "Underplating along the northern portion of the Zagros suture zone, Iran", Geophys. J. Int., 210(1), 375-389. https://doi.org/10.1093/gji/ggx168   DOI
13 Liu, W., Zhang, X., Li, H. and Chen, J. (2020), "Investigation on the deformation and strength characteristics of rock salt under different confining pressures", Geotech. Geol. Eng. https://doi.org/10.1007/s10706-020-01388-1
14 Kargar, P., Osouli, A., Vaughn, B., Hosseini, A. and Rostami, H. (2020), "Feasibility Study of Collapse Remediation of Illinois Loess Using Electrokinetics Technique by Nanosilica and Salt", Proceedings of Geo-Congress 2020, Foundations, Soil Improvement, and Erosion, Reston, VA, USA, Febraury, pp. 667-675. https://doi.org/10.1061/9780784482780.066
15 Karimzadeh, S., Cakir, Z., Osmanoglu, B., Schmalzle, G., Miyajima, M., Amiraslanzadeh, R. and Djamour, Y. (2013), "Interseismic strain accumulation across the North Tabriz Fault (NW Iran) deduced from InSAR time series", J. Geodyn., 66, 53-58. https://doi.org/10.1016/j.jog.2013.02.003   DOI
16 Karimzadeh, S., Matsuoka, M., Miyajima, M., Adriano, B., Fallahi, A. and Karashi, J. (2018), "Sequential SAR coherence method for the monitoring of buildings in Sarpole-Zahab, Iran", Remote Sensing, 10(8), 1255. https://doi.org/10.3390/rs10081255   DOI
17 Kallel, A., Erguler, Z.A., Cui, Z.D., Karrech, A., Karakus, M., Kulatilake, P. and Shukla, S.K. (2019), Recent Advances in Geo-Environmental Engineering, Geomechanics and Geotechnics, and Geohazards, Springer, Cham, pp. 481-483.
18 Li, Z. and Bethel, J. (2008), "Image coregistration in SAR interferometry", Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci., 37, 433-438.
19 Mei, D.P. (2017), "Structural health monitoring-based dynamic behavior evaluation of a long-span high-speed railway bridge", Smart Struct. Syst., Int. J., 20(2), 197-205. https://doi.org/10.12989/sss.2017.20.2.197
20 Moayedi, H., Huat, B.B., Ali, T.A.M., Asadi, A., Moayedi, F. and Mokhberi, M. (2011), "Preventing landslides in times of rainfall: case study and FEM analyses", Disaster Prev. Manage.: Int. J, 20(2), 115-124. https://doi.org/10.1108/09653561111126067   DOI
21 Hugenholtz, C.H., Whitehead, K., Brown, O.W., Barchyn, T.E., Moorman, B.J., LeClair, A., Riddell, K. and Hamilton, T. (2013), "Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model", Geomorphology, 194, 16-24. https://doi.org/10.1016/j.geomorph.2013.03.023   DOI
22 Ferretti, A., Prati, C. and Rocca, F. (1999), "Permanent scatterers in SAR interferometry", Proceedings of IEEE 1999 International Geoscience and Remote Sensing Symposium, IGARSS'99, (Cat. No.99CH36293), Vol.1523, pp. 1528-1530.
23 Glenn, N.F., Streutker, D.R., Chadwick, D.J., Thackray, G.D. and Dorsch, S.J. (2006), "Analysis of LiDAR-Derived Topographic Information for Characterizing and Differentiating Landslide Morphology and Activity", Geomorphology, 73(1). https://doi.org/10.1016/j.geomorph.2005.07.006
24 Guha-Sapir, D., Vos, F., Below, R. and Ponserre, S. (2012), "Annual disaster statistical review 2011: the numbers and trends", In: Centre for Research on the Epidemiology of Disasters (CRED), C.f.R.o.t.E.o.D. (Ed.).
25 He, L., Tan, J., Hu, Q., He, S., Cai, Q., Fu, Y. and Tang, S. (2018), "Non-contact measurement of the surface displacement of a slope based on a smart binocular vision system", Sensors, 18(9), 2890. https://doi.org/10.3390/s18092890   DOI
26 Hooper, A. (2008), "A multi-temporal InSAR method is incorporating both persistent scatterer and small baseline approaches", Geophys. Res. Lett., 35(16). https://doi.org/10.1029/2008GL034654
27 Jinlong, L., Wenjie, X., Jianjing, Z., Wei, L., Xilin, S. and Chunhe, Y. (2020), "Modeling the mining of energy storage salt caverns using a structural dynamic mesh", Energy, 193, 116730. https://doi.org/10.1016/j.energy.2019.116730   DOI
28 Pawluszek-Filipiak, K. and Borkowski, A. (2020), "Integration of DInSAR and SBAS Techniques to Determine Mining-Related Deformations Using Sentinel-1 Data: The Case Study of Rydultowy Mine in Poland", Remote Sensing, 12(2), 242. https://doi.org/10.3390/rs12020242   DOI
29 Nguyen, H., Mehrabi, M., Kalantar, B., Moayedi, H. and Abdullahi, M.A.M. (2019), "Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping", Geomat. Natural Hazards Risk, 10(1), 1667-1693. https://doi.org/10.1080/19475705.2019.1607782   DOI
30 Oveisi, B., Lave, J., Van Der Beek, P., Carcaillet, J., Benedetti, L. and Aubourg, C. (2009), "Thick-and thin-skinned deformation rates in the central Zagros simple folded zone (Iran) indicated by displacement of geomorphic surfaces", Geophys. J. Int., 176(2), 627-654. https://doi.org/10.1111/j.1365-246X.2008.04002.x   DOI
31 Perissin, D. (2019), SARPROZ Manual.
32 Qiao, W. and Yang, Z. (2019a), "Modified dolphin swarm algorithm based on chaotic maps for solving high-dimensional function optimization problems", IEEE Access, 7, 110472-110486. https://doi.org/10.1109/ACCESS.2019.2931910   DOI
33 Qiao, W. and Yang, Z. (2019b), "Solving large-scale function optimization problem by using a new metaheuristic algorithm based on quantum dolphin swarm algorithm", IEEE Access, 7, 138972-138989. https://doi.org/10.1109/ACCESS.2019.2942169   DOI
34 Qiao, W., Huang, K., Azimi, M. and Han, S. (2019a), "A novel hybrid prediction model for hourly gas consumption in supply side based on improved whale optimization algorithm and relevance vector machine", IEEE Access, 7, 88218-88230. https://doi.org/10.1109/ACCESS.2019.2918156   DOI
35 Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019c), "A novel swarm intelligence-Harris hawks optimization for spatial assessment of landslide susceptibility", Sensors, 19(16), 3590. https://doi.org/10.3390/s19163590   DOI
36 Berberian, M. (1995), "Master "blind" thrust faults hidden under the Zagros folds: active basement tectonics and surface morphotectonics", Tectonophysics, 241(3-4), 193-224. https://doi.org/10.1016/0040-1951(94)00185-C   DOI
37 Bui, D.T., Moayedi, H., Gor, M., Jaafari, A. and Foong, L.K. (2019a), "Predicting slope stability failure through machine learning paradigms", ISPRS Int. J. Geo-Inform., 8(9), 395. https://doi.org/10.3390/ijgi8090395   DOI
38 Bui, D.T., Moayedi, H., Kalantar, B., Osouli, A., Gor, M., Pradhan, B., Nguyen, H. and Rashid, A.S.A. (2019b), "Harris hawks optimization: A novel swarm intelligence technique for spatial assessment of landslide susceptibility", Sensors, 19, 3590.   DOI
39 Chen, Q., Cheng, H., Yang, Y., Liu, G. and Liu, L. (2014), "Quantification of mass wasting volume associated with the giant landslide Daguangbao induced by the 2008 Wenchuan earthquake from persistent scatterer InSAR", Remote Sens. Environ., 152, 125-135. https://doi.org/10.1016/j.rse.2014.06.002   DOI
40 Chen, H., Zhang, Q., Luo, J., Xu, Y. and Zhang, X. (2020), "An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine", Appl. Soft Comput., 86, 105884. https://doi.org/10.1016/j.asoc.2019.105884   DOI
41 Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., Yang, B. and Liu, D. (2016), "Evolving support vector machines using fruit fly optimization for medical data classification", Knowledge-Based Syst., 96, 61-75. https://doi.org/10.1016/j.knosys.2016.01.002   DOI
42 Qiao, W., Tian, W., Tian, Y., Yang, Q., Wang, Y. and Zhang, J. (2019b), "The forecasting of PM2. 5 using a hybrid model based on wavelet transform and an improved deep learning algorithm", IEEE Access, 7, 142814-142825. https://doi.org/10.1109/ACCESS.2019.2944755   DOI
43 Rostami, H., Baziar, M.H. and Alibolandi, M. (2018), "Reevaluation of SPT-Based Liquefaction Case History Using Earthquake Demand Energy", Geotechnical Earthquake Engineering and Soil Dynamics V: Liquefaction Triggering, Consequences, and Mitigation, Reston, VA, USA, pp. 493-501. https://doi.org/10.1061/9780784481455.047
44 Schumann, G.J.P. (2017), Remote Sensing of Floods, Oxford University Press.
45 Shi, X., Zhang, L., Zhong, Y., Zhang, L. and Liao, M. (2020), "Detection and characterization of active slope deformations with Sentinel-1 InSAR analyses in the Southwest Area of Shanxi, China", Remote Sensing, 12(3), 392. https://doi.org/10.3390/rs12030392   DOI
46 Stumpf, A. and Kerle, N. (2011), "Object-oriented mapping of landslides using Random Forests", Remote Sens. Environ., 115(10), 2564-2577. https://doi.org/10.1016/j.rse.2011.05.013   DOI
47 Taleshi, A.A., Arab-Amiri, A., Ebrahimi, M. and Abbasinia, M. (2016b), "Two and three-dimonsional ERT modelling for a buried tunnel", J. Emerg. Trends Eng. Appl. Sci., 7(3), 118-127.
48 Tsai, T.L. and Chen, H.F. (2010), "Effects of degree of saturation on shallow landslides triggered by rainfall", Environ. Earth Sci., 59(6), 1285-1295. https://doi.org/10.1007/s12665-009-0116-3   DOI
49 Valerio, E., Tizzani, P., Carminati, E., Doglioni, C., Pepe, S., Petricca, P., De Luca, C., Bignami, C., Solaro, G., Castaldo, R. and De Novellis, V. (2018), "Ground Deformation and Source Geometry of the 30 October 2016 Mw 6.5 Norcia Earthquake (Central Italy) Investigated Through Seismological Data, DInSAR Measurements, and Numerical Modelling", Remote Sensing, 10(12), 1901. https://doi.org/10.3390/rs10121901   DOI
50 Choi, J.C., Lee, S.R., Kim, Y. and Song, Y.H. (2011), "Real-time unsaturated slope reliability assessment considering variations in monitored matric suction", Smart Struct. Syst., Int. J., 7(4), 263-274. https://doi.org/10.12989/sss.2011.7.4.263   DOI
51 Van Dao, D., Jaafari, A., Bayat, M., Mafi-Gholami, D., Qi, C., Moayedi, H., Van Phong, T., Ly, H.B., Le, T.T., Trinh, P.T. and Luu, C. (2020), "A spatially explicit deep learning neural network model for the prediction of landslide susceptibility", CATENA, 188, 104451. https://doi.org/10.1016/j.catena.2019.104451   DOI
52 Vernant, P., Nilforoushan, F., Hatzfeld, D., Abbassi, M.R., Vigny, C., Masson, F., Nankali, H., Martinod, J., Ashtiani, A., Bayer, R. and Tavakoli, F. (2004), "Present-day crustal deformation and plate kinematics in the Middle East constrained by GPS measurements in Iran and northern Oman", Geophys. J. Int., 157(1), 381-398. https://doi.org/10.1111/j.1365-246X.2004.02222.x   DOI
53 Wang, M. and Chen, H. (2020), "Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis", Appl. Soft Comput., 88, 105946. https://doi.org/10.1016/j.asoc.2019.105946   DOI
54 Wang, M., Chen, H., Yang, B., Zhao, X., Hu, L., Cai, Z., Huang, H. and Tong, C. (2017), "Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses", Neurocomputing, 267, 69-84. https://doi.org/10.1016/j.neucom.2017.04.060   DOI
55 Wang, B., Zhao, C., Zhang, Q. and Peng, M. (2019), "Sequential InSAR Time Series Deformation Monitoring of Land Subsidence and Rebound in Xi'an, China", Remote Sensing, 11(23), 2854. https://doi.org/10.3390/rs11232854   DOI
56 Xu, Y., Chen, H., Luo, J., Zhang, Q., Jiao, S. and Zhang, X. (2019), "Enhanced Moth-flame optimizer with mutation strategy for global optimization", Inform. Sci., 492, 181-203. https://doi.org/10.1016/j.ins.2019.04.022   DOI
57 Wang, H., Moayedi, H. and Foong, L.K. (2020), "Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design", Eng. Comput., 1-12. https://doi.org/10.1007/s00366-020-00957-5
58 Wright, T.J., Parsons, B.E. and Lu, Z. (2004), "Toward mapping surface deformation in three dimensions using InSAR", Geophys. Res. Lett., 31(1). https://doi.org/10.1029/2003GL018827
59 Xu, X. and Chen, H.-L. (2014), "Adaptive computational chemotaxis based on field in bacterial foraging optimization", Soft Computing, 18(4), 797-807. https://doi.org/10.1007/s00500-013-1089-4   DOI
60 Yang, Y.J., Hwang, C., Hung, W.C., Fuhrmann, T., Chen, Y.A. and Wei, S.H. (2019), "Surface deformation from sentinel-1A InSAR: Relation to seasonal groundwater extraction and rainfall in central Taiwan", Remote Sensing, 11(23), 2817. https://doi.org/10.3390/rs11232817   DOI
61 Yuan, C. and Moayedi, H. (2019a), "Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for prediction of landslide occurrence", Eng. Comput, 36, 1-11. https://doi.org/10.1007/s00366-019-00798-x
62 Yuan, C. and Moayedi, H. (2019b), "Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence", Eng Comput., 1-11. https://doi.org/10.1007/s00366-019-00798-x
63 Zhang, C.C., Zhu, H.H., Shi, B., She, J.K. and Zhang, D. (2016), "Performance evaluation of soil-embedded plastic optical fiber sensors for geotechnical monitoring", Smart Struct. Syst., Int. J., 17(2), 297-311. https://doi.org/10.12989/sss.2016.17.2.297   DOI
64 Du, Y., Feng, G., Liu, L., Fu, H., Peng, X. and Wen, D. (2020), "Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data", Remote Sensing, 12(2) 299.   DOI
65 Dai, K., Li, Z., Tomas, R., Liu, G., Yu, B., Wang, X., Cheng, H., Chen, J. and Stockamp, J. (2016), "Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry", Remote Sens. Environ., 186, 501-513. https://doi.org/10.1016/j.rse.2016.09.009   DOI
66 Debella-Gilo, M. and Kaab, A. (2012), "Measurement of surface displacement and deformation of mass movements using least squares matching of repeat high resolution satellite and aerial images", Remote Sensing, 4(1) 43-67. https://doi.org/10.3390/rs4010043   DOI
67 Dicelis, G., Assumpcao, M., Kellogg, J., Pedraza, P. and Dias, F. (2016), "Estimating the 2008 Quetame (Colombia) earthquake source parameters from seismic data and InSAR measurements", J. South Am. Earth Sci., 7, 250-265. https://doi.org/10.1016/j.jsames.2016.09.011
68 Du, Y., Feng, G., Liu, L., Fu, H., Peng, X. and Wen, D. Ebrahimi, M., Moradi, A., Bejvani, M. and Tafreshi, M.D. (2016), "Application of STA/LTA Based on Cross-Correlation to Passive Seismic Data", Proceedings of the 6th EAGE Workshop on Passive Seismic, Muscat, Oman, January, pp. 1-5.
69 European Space Agency (2016), Interferometry Tutorial.
70 Agard, P., Omrani, J., Jolivet, L. and Mouthereau, F. (2005), "Convergence history across Zagros (Iran): constraints from collisional and earlier deformation", Int. J. Earth Sci., 94(3), 401-419. https://doi.org/10.1007/s00531-005-0481-4   DOI
71 Zhou, G., Moayedi, H. and Foong, L.K. (2020b), "Teaching-learning-based metaheuristic scheme for modifying neural computing in appraising energy performance of building", Eng. Comput., 36. https://doi.org/10.1007/s00366-020-00981-5
72 Zhang, T., Shen, W.B., Wu, W., Zhang, B. and Pan, Y. (2019a), "Recent surface deformation in the Tianjin area revealed by Sentinel-1A data", Remote Sensing, 11(2), 130. https://doi.org/10.3390/rs11020130   DOI
73 Zhang, Z., Jiang, D., Liu, W., Chen, J., Li, E., Fan, J. and Xie, K. (2019b), "Study on the mechanism of roof collapse and leakage of horizontal cavern in thinly bedded salt rocks", Environ. Earth Sci., 78(10), 292. https://doi.org/10.1007/s12665-019-8292-2   DOI
74 Zhou, G., Moayedi, H., Bahiraei, M. and Lyu, Z. (2020a), "Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings", J. Cleaner Prod., 254. https://doi.org/10.1016/j.jclepro.2020.120082
75 Zhu, H.H., Ho, A.N., Yin, J.H., Sun, H.W., Pei, H.F. and Hong, C.Y. (2012), "An optical fibre monitoring system for evaluating the performance of a soil nailed slope", Smart Struct. Syst., Int. J., 9(5), 393-410. https://doi.org/10.12989/sss.2012.9.5.393   DOI