Abstract
Surface subsidence threatens the structural stability of ground facilities located in mining-induced subsidence areas. Clarifying and evaluating the influence of surface subsidence can inform the construction and maintenance of various ground facilities, such as buildings, roads, and bridges. In this paper, we investigated mining-induced surface subsidence and areas of potential damage in Yangquan City, Shanxi Province, by exploiting small-baseline set interferometric synthetic aperture radar (SBAS-InSAR) monitoring and geographic information system (GIS) techniques. More specifically, we first investigated the distributions of subsidence areas and subsidence rates in Yangquan City from June 16th, 2016, to December 1st, 2016, by exploiting SBAS-InSAR monitoring. We then classified ground facilities, such as buildings, highways and railways, and identified their distributions using spatial analysis using GIS. Finally, we integrated the results of the two techniques to evaluate the potential damages induced by surface subsidence for various ground facilities. We found that, overall, (1) surface subsidence has seriously developed in the Yangquan Mine and (2) some of the subsidence areas exist in facilities with high-level restrictions, such as high-rise buildings, highways, and railways, which may cause potential damage. Our work presented in this paper could be referred to and applied to other similar cases.
Similar content being viewed by others
References
Autin WJ (2002) Landscape evolution of the five Islands of South Louisiana: scientific policy and salt dome utilization and management. Geomorphology 47(2):227–244
Bastiaanssen WGM, Ali S (2003) A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric Ecosyst Environ 94(3):321–340
Bateson L, Cigna F, Boon D, Sowter A (2015) The application of the intermittent SBAS (ISBAS) InSAR method to the South Wales Coalfield, UK. Int J Appl Earth Obs Geoinf 34:249–257
Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development using natural hazard maps and geological-geomorphological parameters. Environ Earth Sci 66:537–548
Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119–134
Bathrellos GD, Skilodimou HD (2019) Land use planning for natural hazards. Land 8:128–128
Berardino P, Fornaro G, Lanari R, Sansosti E (2002) A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE T Geosci Remote Sens 40(11):2375–2383
Blasco JMD, Foumelis M, Stewart C, Hooper A (2019) Measuring urban subsidence in the Rome metropolitan area (Italy) with Sentinel-1 SNAP-StaMPS persistent scatterer interferometry. Remote Sens 11(2):1–17
Bui DT, Tuan TA, Klempe H (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378
Bui DT, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, Khosravi K, Panahi M, Ahmad BB, Saro L (2018) Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors 18(8):1–20
Can E, Kuşcu Ş, Mekik C (2012) Determination of underground mining induced displacements using GPS observations in Zonguldak-Kozlu Hard Coal Basin. Int J Coal Geol 89:62–69
Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):1–18
Carleer A, Wolff E (2006) Urban land cover multi-level region-based classification of VHR data by selecting relevant features. Int J Remote Sens 27(6):1035–1051
Cascini L, Peduto D, Reale D, Arena L, Ferlisi S, Verde S, Fornaro G (2013) Detection and monitoring of facilities exposed to subsidence phenomena via past and current generation SAR sensors. J Geophys Eng 10(6):1–21
Casu F, Manzo M, Lanari R (2006) A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sens Environ 102(3):195–210
Chen F, Lin H, Zhang Y, Lu Z (2012) Ground subsidence geo-hazards induced by rapid urbanization: implications from InSAR observation and geological analysis. Nat Hazard Earth Syst 12(4):935–942
Colesanti C, Ferretti A, Novali F, Prati C, Rocca F (2003) SAR monitoring of progressive and seasonal ground deformation using the permanent scatterers technique. IEEE T Geosci Remote Sens 41(7):1685–1701
Cuomo S, De Michele P, Piccialli F, Sangaiah AK (2018) Reproducing dynamics related to an internet of things framework: a numerical and statistical approach. J Parallel Distrib Comput 118:359–368
Daly C, Neilson RP, Phillips DL (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol 33(2):140–158
Davies DK, Ilavajhala S, Wong MM, Justice CO (2009) Fire information for resource management system: archiving and distributing MODIS active dire data. IEEE T Geosci Remote Sens 47(1):72–79
Dewan AM, Yamaguchi Y (2009) Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl Geogr 29(3):390–401
Dong J, Li H, Wang Y (2021) Characteristics and monitoring-based analysis on deformation mechanism of Jianshanying landslide, Guizhou Province, southwestern China. Arab J Geosci 14:184
Dong S, Samsonov S, Yin H, Ye S, Cao Y (2014) Time-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method. Environ Earth Sci 72(3):677–691
Ferretti A, Prati C, Rocca F (2000) Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE T Geosci Remote Sens 38(5):2202–2212
Fielding EJ, Blom RG, Goldstein RM (1998) Rapid subsidence over oil fields measured by SAR interferometry. Geophys Res Lett 25(17):3215–3218
Gabriel AK, Goldstein RM, Zebker HA (1989) Mapping small elevation changes over large areas: differential radar interferometry. J Geophys Res Solid Earth 94(B7):9183–9191
Galve JP, Gutiérrez F, Guerrero J, Alonso J, Diego I (2012) Optimizing the application of geosynthetics to roads in sinkhole-prone areas on the basis of hazard models and cost-benefit analyses. Geotext Geomembr 34:80–92
Goldstein RM, Werner CL (1998) Radar interferogram filtering for geophysical applications. Geophys Res Lett 25(21):4035–4038
Guerrero J, Gutiérrez F, Bonachea J, Lucha P (2008) A sinkhole susceptibility zonation based on paleokarst analysis along a stretch of the Madrid-Barcelona high-speed railway built over gypsum- and salt-bearing evaporites (NE Spain). Eng Geol 102(1):62–73
Gui H, Sun L, Chen S (2016) Research on goaf water features and disaster formation mechanism in China coalmines. IOP Conf Ser Earth Environ Sci 44:36–42
Hu B, Li H, Zhang X, Fang L (2020) Oil and gas mining deformation monitoring and assessments of disaster: using interferometric synthetic aperture radar technology. IEEE Geosci Remote Sens 8(2):1–27
Hu J, Li ZW, Ding XL, Zhu JJ, Zhang L, Sun Q (2014) Resolving three-dimensional surface displacements from InSAR measurements: a review. Earth Sci Rev 133:1–17
Ilieva M, Polanin P, Borkowski A, Gruchlik P, Smolak K, Kowalski A, Rohm W (2019) Mining deformation life cycle in the light of InSAR and deformation models. Remote Sens 11(7):1–30
Irizarry J, Karan EP, Jalaei F (2013) Integrating BIM and GIS to improve the visual monitoring of construction supply chain management. Autom Constr 31:241–254
Koros WK, Agustin F (2016) Subsidence surveys at Olkaria geothermal field, Kenya. J Spat Sci 62(1):1–11
Liu P, Li Z, Hoey T, Kincal C, Zhang J, Zeng Q, Muller J (2013) Using advanced InSAR time series techniques to monitor landslide movements in Badong of the Three Gorges region, China. Int J Appl Earth Obs Geoinf 21:253–264
Malinowska A, Witkowski W, Guzy A, Hejmanowski R (2020) Satellite-based monitoring and modeling of ground movements caused by water rebound. Remote Sens 12(11):1–17
Mancini F, Stecchi F, Zanni M, Gabbianelli G (2009) Monitoring ground subsidence induced by salt mining in the city of Tuzla (Bosnia and Herzegovina). Environ Geol 58:381–389
Massonnet D, Rossi M, Carmona C, Adragna F, Peltzer G (1993) The displacement field of the Landers earthquake mapped by radar interferometry. Nature 364:138–142
Ng A, Ge L, Zhang K, Chang H-C, Li X, Rizos C, Omura M (2011) Deformation mapping in three dimensions for underground mining using InSAR-Southern highland coalfield in New South Wales, Australia. Int J Remote Sens 32(22):7227–7256
Papadopoulou-Vrynioti K, Bathrellos GD, Skilodimou HD, Kaviris G, Makropoulos K (2013) Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area. Eng Geol 158:77–88
Peduto D, Cascini L, Arena L, Ferlisi S, Fornaro G, Reale D (2015) A general framework and related procedures for multiscale analyses of DInSAR data in subsiding urban areas. ISPRS J Photogramm Remote Sens 105:186–210
Pepe A, Lanari R (2006) On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms. IEEE Trans Geosci Remote Sens 44(9):2374–2383
Piccialli F, Jung JE (2017) Understanding customer experience diffusion on social networking services by big data analytics. Mob Netw Appl 22:605–612
Piccialli F, Jung JJ (2018) Data fusion in the internet of data. Concurr Comput Pract Exp 30(15):e4700
Piccialli F, Casolla G, Cuomo S, Giampaolo F, di Cola VS (2020a) Decision making in IoT environment through unsupervised learning. IEEE Intell Syst 35(1):27–35
Piccialli F, Cuomo S, Bessis N, Yoshimura Y (2020b) Data science for the internet of things. IEEE IoT J 7(5):4342–4346
Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365
Qin X, Yang M, Zhang L, Yang T, Liao M (2017) Health diagnosis of major transportation infrastructures in shanghai metropolis using high-resolution persistent scatterer interferometry. Sensors 17(12):1–25
Querol X, Izquierdo M, Monfort E, Alvarez E, Font O, Moreno T, Alastuey A, Zhuang X, Lu W, Wang Y (2008) Environmental characterization of burnt coal gangue banks at Yangquan, Shanxi Province, China. Int J Coal Geol 75(2):93–104
Saleh M, Becker M (2018) New estimation of Nile Delta subsidence rates from InSAR and GPS analysis. Environ Earth Sci 78(1):6–6
Sano E, Rosa R, Brito J (2010) Land cover mapping of the tropical savanna region in Brazil. Environ Monit Assess 166:113–124
Shafizadeh-Moghadam H, Minaei M, Shahabi H, Hagenauer J (2019) Big data in geohazard; pattern mining and large scale analysis of landslides in Iran. Earth Sci Inform 12(1):1–17
Shalaby A, Tateishi R (2007) Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the northwestern coastal zone of Egypt. Appl Geogr 27(1):28–41
Shuran L, Shujin L (2011) Research on governance of potential safety hazard in Da’an mine goaf. Proced Eng 26:351–356
Skilodimou HD, Bathrellos GD, Chousianitis K, Youssef AM, Pradhan B (2019) Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environ Earth Sci 78:47–78
Solaro G, Acocella V, Pepe S, Ruch J, Neri M, Sansosti E (2010) Anatomy of an unstable volcano from InSAR: multiple processes affecting flank instability at Mt. Etna, 1994–2008. J Geophys Res Solid Earth 115(B10):1–21
Tesauro M, Berardino P, Lanari R, Sansosti E, Fornaro G, Franceschetti G (2000) Urban subsidence inside the city of Napoli (Italy) observed by satellite radar interferometry. Geophys Res Lett 27(13):1961–1964
Thomas MR (2002) A GIS-based decision support system for brownfield redevelopment. Landsc Urban Plan 58(1):7–23
Tizzani P, Berardino P, Casu F, Euillades P, Manzo M, Ricciardi G, Zeni G (2007) Surface deformation of Long Valley caldera and Mono Basin, California, investigated with the SBAS-InSAR approach. Remote Sens Environ 108(3):277–289
Vervoort A, Declercq P-Y (2018) Upward surface movement above deep coal mines after closure and flooding of underground workings. Int J Min Sci Technol 28(1):53–59
Walter V (2004) Object-based classification of remote sensing data for change detection. ISPRS J Photogramm Remote Sens 58(3):225–238
Wu Q, Wu Q, Xue Y, Kong P, Gong B (2018) Analysis of overlying strata movement and disaster-causing effects of coal mining face under the action of hard thick magmatic rock. Processes 6(9):1–18
Xia Y, Wang Y, Du S, Liu X, Zhou H (2018) Integration of D-InSAR and GIS technology for identifying illegal underground mining in Yangquan District, Shanxi Province, China. Environ Earth Sci 77(8):319–319
Xu C, Liu Y, Wen Y, Wang R (2010) Coseismic slip distribution of the 2008 M-w 7.9 Wenchuan earthquake from joint inversion of GPS and InSAR data. Bull Seismol Soc Am 100:2736–2749
Yang Z, Li Z, Zhu J, Hu J, Wang Y, Chen G (2016) InSAR-based model parameter estimation of probability integral method and its application for predicting mining-induced horizontal and vertical displacements. IEEE T Geosci Remote Sens 54(8):1–15
Yao G, Ke C, Zhang J (2019) Surface deformation monitoring of Shanghai based on ENVISAT ASAR and Sentinel-1A data. Environ Earth Sci 78:225–225
Zeni G, Bonano M, Casu F, Manunta M, Manzo M, Marsella M, Pepe A, Lanari R (2011) Long-term deformation analysis of historical buildings through the advanced SBAS-DInSAR technique: the case study of the city of Rome, Italy. J Geophys Eng 8(3):S1–S12
Zhou D, Wu K, Chen R, Li L (2014) GPS/terrestrial 3D laser scanner combined monitoring technology for coal mining subsidence: a case study of a coal mining area in Hebei, China. Nat Hazards 70(2):1197–1208
Acknowledgements
This research was jointly supported by the National Natural Science Foundation of China (Grant Nos. 11602235 and 41772326), the Fundamental Research Funds for China Central Universities (2652018091), the Geological Survey Project of CGS (DD20190593), and Major Program of Science and Technology of Xinjiang Production and Construction Corps (2020AA002).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, Z., Mei, G., Sun, Y. et al. Investigating mining-induced surface subsidence and potential damages based on SBAS-InSAR monitoring and GIS techniques: a case study. Environ Earth Sci 80, 817 (2021). https://doi.org/10.1007/s12665-021-09726-z
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-021-09726-z