SavedAdd your journal

Journals / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 8

Ground deformation monitoring: A bibliometric analysis of recent trends and techniques

Burak Can Kara(AMASYA ÜNİVERSİTESİ, MERZİFON MESLEK YÜKSEKOKULU)0000-0002-6933-0759
Kemal Özgür Hastaoğlu(SİVAS CUMHURİYET ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, GEOMATİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-5077-5889
Pages
—
Publication Date
2026-03-25
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These trends highlight the importance of methodological standardization, data fusion, and artificial intelligence for advancing ground deformation monitoring.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Ground deformation monitoring is a critical component in understanding natural and anthropogenic processes that impact infrastructure safety, disaster risk, and sustainable resource management.
📊
Key Findings & Data:A bibliometric analysis was conducted using data from the Web of Science and Scopus databases, covering 3,616 articles published between 1984 and 2024.
💡
Conclusion & Impact:These trends highlight the importance of methodological standardization, data fusion, and artificial intelligence for advancing ground deformation monitoring.
🔬Design:Modeling & Simulation
📍Scope:2024 Verileri
⚙️Variables:Ground deformation · Artificial intelligence · External forces

Abstract

Ground deformation monitoring is a critical component in understanding natural and anthropogenic processes that impact infrastructure safety, disaster risk, and sustainable resource management. The rapid growth in remote sensing and geodetic measurement techniques over the past four decades has led to a complex and expanding body of research. This study aims to systematically map recent trends, techniques, and research gaps in ground deformation monitoring, addressing the need for a comprehensive synthesis of methodological advances and thematic priorities. A bibliometric analysis was conducted using data from the Web of Science and Scopus databases, covering 3,616 articles published between 1984 and 2024. Data processing included keyword filtering and discipline-based selection to ensure relevance. Results reveal that interferometric synthetic aperture radar and global navigation satellite systems are the most prominent techniques, while machine learning and deep learning approaches have gained significant momentum after 2020. International collaboration rates have reached 33%, with China, the United States, and Italy leading in publication output and impact. The findings demonstrate a shift toward integrated, data-intensive, and interdisciplinary research, emphasizing decision support and risk management applications. These trends highlight the importance of methodological standardization, data fusion, and artificial intelligence for advancing ground deformation monitoring.

🔖

Cite This Article

Kara, B. C., Hastaoğlu, K. Ö. (2026). Ground deformation monitoring: A bibliometric analysis of recent trends and techniques. Türkiye Uzaktan Algılama Dergisi, Volume: 8. https://doi.org/10.51489/tuzal.1754180

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (83)

  1. Abdalla, A., Shami, S., Shahriari, M. A., & Azar, M. K. (2024). Estimation of land displacement in East Baton Rouge Parish, Louisiana, using InSAR: Comparisons with GNSS and machine learning models. The Egyptian Journal of Remote Sensing and Space Sciences, 27(2), 204–215. https://doi.org/10.1016/j.ejrs.2024.02.008
  2. Ager, T. P. (2013). An introduction to synthetic aperture radar imaging. Oceanography, 26(2), 20–33.
  3. Ali, M. Z., Chu, H., Tatas, & Burbey, T. J. (2022). Estimation of annual groundwater changes from InSAR‐derived land subsidence. Water and Environment Journal, 36(4), 622–632. https://doi.org/10.1111/wej.12802
  4. An, Y., Yang, F., Xu, J., Ren, C., Hu, J., & Luo, G. (2024). Analysis and Prediction of Temporal and Spatial Evolution of Groundwater Storage by Combined SAR-GRACE Satellite Data. IEEE Access, 12, 33671–33686. https://doi.org/10.1109/ACCESS.2024.3368423
  5. Anghel, M., Ben-Zion, Y., & Rico-Martinez, R. (2004). Dynamical System Analysis and Forecasting of Deformation Produced by an Earthquake Fault. Pure and Applied Geophysics, 161(9–10), 2023–2051. https://doi.org/10.1007/s00024-004-2547-9
  6. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  7. Bao, X., Zhang, R., Shama, A., Li, S., Xie, L., Lv, J., Fu, Y., Wu, R., & Liu, G. (2022). Ground deformation pattern analysis and evolution prediction of Sshanghai Pudong international airport based on PSI long time series observations. Remote Sensing, 14(3), 610. https://doi.org/10.3390/rs14030610
  8. Baraçlı, H., Şahin, A., & Dönmez, C. Ç. (2024). The examination of studies in higher education in turkey in the context of internationalization: A bibliometric analysis. Electronic Journal of Social Sciences, 23(89), 282–304. https://doi.org/10.17755/esosder.1340136
  9. Baryla, R. (2014). Accuracy assessment of the ground deformation monitoring with the use of GPS local network: Open pit mine Kozmin case study. Acta Geodynamica et Geomaterialia, 317–324. https://doi.org/10.13168/AGG.2014.0013
  10. Bayramov, E., Buchroithner, M., & Kada, M. (2020). Quantitative assessment of ground deformations for the risk management of petroleum and gas pipelines using radar interferometry. Geomatics, Natural Hazards and Risk, 11(1), 2540–2568. https://doi.org/10.1080/19475705.2020.185361
  11. Bayramov, E., Buchroithner, M., Kada, M., & Bayramov, R. (2022a). Quantitative assessment of ground deformation risks, controlling factors and movement trends for onshore petroleum and gas industry using satellite Radar remote sensing and spatial statistics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 16(2), 283–300. https://doi.org/10.1080/17499518.2020.1806334
  12. Bayramov, E., Tessari, G., & Kada, M. (2022b). Quantifying Two-Dimensional Surface Displacements Using High-Resolution Cosmo-SkyMed, TerraSAR-X and Medium-Resolution Sentinel-1 SAR Interferometry: Case study for the Tengiz Oilfield. Sensors, 22(17), 6416. https://doi.org/10.3390/s22176416
  13. Chen, B., Yu, H., Zhang, X., Li, Z., Kang, J., Yu, Y., Yang, J., & Qin, L. (2022). Time-Varying Surface Deformation Retrieval and Prediction in Closed Mines through Integration of SBAS InSAR Measurements and LSTM Algorithm. Remote Sensing, 14(3), 788. https://doi.org/10.3390/rs14030788
  14. Chen, Y., Du, S., Huang, P., Ren, H., Yin, B., Qi, Y., Ding, C., & Xu, W. (2024a). Analysis and prediction of urban surface transformation based on small baseline subset interferometric synthetic aperture radar and sparrow search algorithm–convolutional neural network–long short-term memory model. Sensors, 24(8), 2634. https://doi.org/10.3390/s24082634
  15. Chen, J., Lin, X., Wu, T., Hao, J., Wu, X., Zou, D., Zhu, X., Hu, G., Qiao, Y., Wang, D., Yang, S., & Zhang, L. (2024b). Predicting seasonal deformation using InSAR and machine learning in the permafrost regions of the Yangtze River source region. Water Resources Research, 60(9), e2023WR036700. https://doi.org/10.1029/2023WR036700
  16. Chen, Y., Norde, W., Van Der Mei, H. C., & Busscher, H. J. (2012). Bacterial cell surface deformation under external loading. mBio, 3(6), e00378-12. https://doi.org/10.1128/mBio.00378-12
  17. Demir, S., & Yomralioglu, T. (2024). Bridging Geo-data and natural gas pipeline design standards: A systematic review of BIM-GIS integration for natural gas pipeline asset management. Energies, 17(10), 2306. https://doi.org/10.3390/en17102306
  18. Derviş, H. (2020). Bibliometric analysis using bibliometrix an R Package. Journal of Scientometric Research, 8(3), 156–160. https://doi.org/10.5530/jscires.8.3.32
  19. Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
  20. Du, S., Song, R., Qu, Q., Zhao, Z., Sun, H., & Chen, Y. (2024). Surface deformation prediction model of high and steep open-pit slope based on APSO and TWSVM. Elektronika Ir Elektrotechnika, 30(1), 77–83. https://doi.org/10.5755/j02.eie.36115
  21. Fang, H., Shao, Y., Xie, C., Tian, B., Zhu, Y., Guo, Y., Yang, Q., & Yang, Y. (2022). Using Persistent Scatterer Interferometry for Post-Earthquake Landslide Susceptibility Mapping in Jiuzhaigou. Applied Sciences, 12(18), 9228. https://doi.org/10.3390/app12189228
  22. Festa, D., Casagli, N., Casu, F., Confuorto, P., De Luca, C., Del Soldato, M., Lanari, R., Manunta, M., Manzo, M., & Raspini, F. (2022). Automated classification of A-DInSAR-based ground deformation by using random forest. GIScience & Remote Sensing, 59(1), 1749–1766. https://doi.org/10.1080/15481603.2022.2134561
  23. Fu, G., Schmid, W., & Castellazzi, P. (2023). Understanding the spatial variability of the relationship between InSAR-derived deformation and groundwater level using machine learning. Geosciences, 13(5), 133. https://doi.org/10.3390/geosciences13050133
  24. González, L.-M., García-Massó, X., Pardo-Ibañez, A., Peset, F., & Devís-Devís, J. (2018). An author keyword analysis for mapping Sport Sciences. PLOS ONE, 13(8), e0201435. https://doi.org/10.1371/journal.pone.0201435
  25. Gu, Z., & Yao, X. (2022). Reconstruction of surface deformation characteristics in alpine canyons under shadow conditions. Journal of Mountain Science, 19(11), 3105–3117. https://doi.org/10.1007/s11629-021-7294-1
  26. Guo, X., Zhao, C., Li, G., Peng, M., & Zhang, Q. (2023). A Multifactor-based random forest regression model to reconstruct a continuous deformation map in Xi’an, China. Remote Sensing, 15(19), 4795. https://doi.org/10.3390/rs15194795
  27. Guo, Y., Hu, S., Wu, W., Wang, Y., & Senthilnath, J. (2020). Multitemporal time series analysis using machine learning models for ground deformation in the Erhai region, China. Environmental Monitoring and Assessment, 192(7), 464. https://doi.org/10.1007/s10661-020-08426-8
  28. Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2017). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence. https://doi.org/10.1007/s10489-017-1105-y
  29. Hastaoğlu, K. Ö., Gül, Y., Poyraz, F., & Kara, B. C. (2019). Monitoring 3D areal displacements by a new methodology and software using UAV photogrammetry. International Journal of Applied Earth Observation and Geoinformation, 83, 101916. https://doi.org/10.1016/j.jag.2019.101916
  30. He, L., Wang, X., Liu, C., Tang, Y., Zhang, X., Kang, J., Guo, C., & He, R. (2022). Time series interferometric synthetic aperture radar-based modeling and analysis of complex land subsidence caused by multi-seam coal mining on the Liaohe Plain, China. Journal of Applied Remote Sensing, 16(02). https://doi.org/10.1117/1.JRS.16.024512
  31. Hu, X., Bürgmann, R., Xu, X., Fielding, E., & Liu, Z. (2021). Machine‐Learning Characterization of Tectonic, Hydrological and Anthropogenic Sources of Active Ground Deformation in California. Journal of Geophysical Research: Solid Earth, 126(11), e2021JB022373. https://doi.org/10.1029/2021JB022373
  32. Ike, H. (2003). Surface deformation vs. Bulk plastic deformation—A key for microscopic control of surfaces in metal forming. Journal of Materials Processing Technology, 138(1–3), 250–255. https://doi.org/10.1016/S0924-0136(03)00080-3
  33. Ilia, I., Loupasakis, C., & Tsangaratos, P. (2018). Land subsidence phenomena investigated by spatiotemporal analysis of groundwater resources, remote sensing techniques, and random forest method: The case of Western Thessaly, Greece. Environmental Monitoring and Assessment, 190(11), 623. https://doi.org/10.1007/s10661-018-6992-9
  34. Kara, B. C. (2025). keyFinder—Filtering and analyzing scientific articles based on specific keywords (Version 1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.15167425
  35. Kara, B. C., Şahin, A., & Dirsehan, T. (2025). BibexPy: Harmonizing the bibliometric symphony of Scopus and Web of Science. SoftwareX, 30, 102098. https://doi.org/10.1016/j.softx.2025.102098
  36. Khalili, M. A., Coda, S., Calcaterra, D., & Di Martire, D. (2024). Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador. Geomatics, Natural Hazards and Risk, 15(1), 2383270. https://doi.org/10.1080/19475705.2024.2383270
  37. Li, F., Gong, H., Chen, B., Zhou, C., & Guo, L. (2020). Analysis of the contribution rate of the influencing factors to land subsidence in the Eastern Beijing Plain, China based on extremely randomized trees (ERT) method. Remote Sensing, 12(18), 2963. https://doi.org/10.3390/rs12182963
  38. Li, Q., Yao, C., Yao, X., Zhou, Z., & Ren, K. (2024a). Time Series prediction of reservoir bank slope deformation based on informer and InSAR: A Case Study of Dawanzi Landslide in the Baihetan Reservoir Area, China. Remote Sensing, 16(15), 2688. https://doi.org/10.3390/rs16152688
  39. Li, P., Wang, H., Li, H., Ni, Z., Deng, H., Sui, H., & Xu, G. (2024b). Refined landslide susceptibility mapping considering land use changes and InSAR deformation: A case study of Yulin City, Guangxi. Remote Sensing, 16(16), 3016. https://doi.org/10.3390/rs16163016
  40. Li, N., Feng, G., Zhao, Y., Xiong, Z., He, L., Wang, X., Wang, W., & An, Q. (2024c). A deep-learning-based algorithm for landslide detection over wide areas using InSAR images considering topographic features. Sensors, 24(14), 4583. https://doi.org/10.3390/s24144583
  41. Lin, Q., Zhang, Z., Yang, Z., Zhang, X., Rong, X., Yang, S., Hao, Y., Zhu, X., & Wang, W. (2024d). Co-seismic landslides susceptibility evaluation of Bayesian random forest considering InSAR deformation: A case study of the Luding Ms6.8 earthquake. Geomatics, Natural Hazards and Risk, 15(1), 2383783. https://doi.org/10.1080/19475705.2024.2383783
  42. Liu, L., Zhou, W., & Gutierrez, M. (2022). Mapping tunneling-induced uneven ground subsidence using Sentinel-1 SAR interferometry: A twin-tunnel case study of downtown Los Angeles, USA. Remote Sensing, 15(1), 202. https://doi.org/10.3390/rs15010202
  43. Liu, X., Ma, J., & Su, S. (2020). Factors influencing activity of xiwang road ground fissure based on time-series interferometric synthetic aperture radar and detection of land cover changes using optical imaging. Sensors and Materials, 32(12), 4635. https://doi.org/10.18494/SAM.2020.3151
  44. Ma, P., Jiao, Z., & Wu., Z. (2024). Robust time-series InSAR deformation monitoring by integrating variational mode decomposition and gated recurrent units. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–15. https://doi.org/10.1109/JSTARS.2024.3426676
  45. Marzi, G., Balzano, M., Caputo, A., & Pellegrini, M. M. (2025). Guidelines for bibliometric-systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. International Journal of Management Reviews, 27(1), 81–103. https://doi.org/10.1111/ijmr.12381
  46. Mohammed, O. I., Saeidi, V., Pradhan, B., & Yusuf, Y. A. (2014). Advanced differential interferometry synthetic aperture radar techniques for deformation monitoring: A review on sensors and recent research development. Geocarto International, 29(5), 536–553. https://doi.org/10.1080/10106049.2013.807305
  47. Naghibi, S. A., Khodaei, B., & Hashemi, H. (2022). An integrated InSAR-machine learning approach for ground deformation rate modeling in arid areas. Journal of Hydrology, 608, 127627. https://doi.org/10.1016/j.jhydrol.2022.127627
  48. Ng, A. H.-M., Ge, L., Chang, H.-C., & Du, Z. (2023). Geodetic monitoring for land deformation. Remote Sensing, 15(1), 283. https://doi.org/10.3390/rs15010283
  49. Osmanoğlu, B., Sunar, F., Wdowinski, S., & Cabral-Cano, E. (2016). Time series analysis of InSAR data: Methods and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 90–102. https://doi.org/10.1016/j.isprsjprs.2015.10.003
  50. Pan, X., Jiao, C., Yang, B., Zhu, H., & Wu, J. (2025). Attribute-guided feature fusion network with knowledge-inspired attention mechanism for multi-source remote sensing classification. Neural Networks, 187, 107332. https://doi.org/10.1016/j.neunet.2025.107332
  51. Passas, I. (2024). Bibliometric Analysis: The main steps. Encyclopedia, 4(2), 1014–1025. https://doi.org/10.3390/encyclopedia4020065
  52. Peng, M., Motagh, M., Lu, Z., Xia, Z., Guo, Z., Zhao, C., & Liu, Q. (2024). Characterization and prediction of InSAR-derived ground motion with ICA-assisted LSTM model. Remote Sensing of Environment, 301, 113923. https://doi.org/10.1016/j.rse.2023.113923
  53. Phoong, S. W., Phoong, S. Y., & Khek, S. L. (2022). Systematic literature review with bibliometric analysis on Markov switching model: methods and applications. Sage Open, 12(2), 21582440221093062. https://doi.org/10.1177/21582440221093062
  54. Pranckutė, R. (2021). Web of Science (WoS) and Scopus: The titans of bibliographic information in today’s academic world. Publications, 9(1), 12. https://doi.org/10.3390/publications9010012
  55. Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25, 348–349.
  56. Pritchard, M. (2010). Deformation explained. Nature Geoscience, 3(8), 515–515. https://doi.org/10.1038/ngeo925
  57. Radman, A., Akhoondzadeh, M., & Hosseiny, B. (2021). Integrating InSAR and deep-learning for modeling and predicting subsidence over the adjacent area of Lake Urmia, Iran. GIScience & Remote Sensing, 58(8), 1413–1433. https://doi.org/10.1080/15481603.2021.1991689
  58. Rajabi, A. M., Edalat, A., Abolghasemi, Y., & Khodaparast, M. (2024). Using neural network modeling to improve the detection accuracy of land subsidence due to groundwater withdrawal. Journal of Mountain Science, 21(7), 2320–2333. https://doi.org/10.1007/s11629-023-8470-2
  59. Ren, T., Gong, W., Gao, L., & Zhao, F. (2024). Understanding land subsidence in the Pearl River Delta region of China based on InSAR observations. Engineering Geology, 339, 107646. https://doi.org/10.1016/j.enggeo.2024.107646
  60. Şahin, A., Kara, B. C., & Dirsehan, T. (2025). LitOrganizer: Automating the process of data extraction and organization for scientific literature reviews. SoftwareX, 30, 102198. https://doi.org/10.1016/j.softx.2025.102198
  61. Sheng, Y., Xu, G., Jin, B., Zhou, C., Li, Y., & Chen, W. (2023). Data-driven landslide spatial prediction and deformation monitoring: A case study of Shiyan City, China. Remote Sensing, 15(21), 5256. https://doi.org/10.3390/rs15215256
  62. Shi, J., Xu, B., Chen, Q., Hu, M., & Zeng, Y. (2022). Monitoring and analysing long-term vertical time-series deformation due to oil and gas extraction using multi-track SAR dataset: A study on lost hills oilfield. International Journal of Applied Earth Observation and Geoinformation, 107, 102679. https://doi.org/10.1016/j.jag.2022.102679
  63. Siccardi, S., & Villa, V. (2022). Trends in Adopting BIM, IoT and DT for facility management: A scientometric analysis and keyword co-occurrence network review. Buildings, 13(1), 15. https://doi.org/10.3390/buildings13010015
  64. Tatar, O., Poyraz, F., Gürsoy, H., Cakir, Z., Ergintav, S., Akpınar, Z., Koçbulut, F., Sezen, F., Türk, T., Hastaoğlu, K. Ö., Polat, A., Mesci, B. L., Gürsoy, Ö., Ayazlı, İ. E., Çakmak, R., Belgen, A., & Yavaşoğlu, H. (2012). Crustal deformation and kinematics of the Eastern part of the North Anatolian Fault Zone (Turkey) from GPS measurements. Tectonophysics, 518–521, 55–62. Https://doi.org/10.1016/j.tecto.2011.11.010s
  65. Teng, C., Wang, L., & Jiang, C. (2024). Urban surface deformation monitoring and prediction by integrating SBAS-InSAR and Elman neural network. Survey Review, 56(394), 18–31. https://doi.org/10.1080/00396265.2022.2157119
  66. Tian, Y., Wang, Z., & Xiao, B. (2023). Multi-Scale Ground deformation analysis and investigation of driver factors based on remote sensing data: A case study of Zhuhai City. Remote Sensing, 15(21), 5155. https://doi.org/10.3390/rs15215155
  67. Wang, F., Wen, Z., Gao, Q., Yu, Q., Li, D., & Chen, L. (2024a). Thermokarst landslides susceptibility evaluation across the permafrost region of the central Qinghai-Tibet Plateau: Integrating a machine learning model with InSAR technology. Journal of Hydrology, 642, 131800. https://doi.org/10.1016/j.jhydrol.2024.131800
  68. Wang, H., Gong, H., Chen, B., Zhou, C., Yang, Y., & Sun, X. (2024b). Research on land subsidence-rebound affected by dualistic water cycle driven by climate change and human activities in Dezhou City, China. Journal of Hydrology, 636, 131327. https://doi.org/10.1016/j.jhydrol.2024.131327
  69. Wu, Z., Zhang, X., Cai, J., Kwan, M.-P., Lin, H., & Ma, P. (2023). Understanding spatially nonstationary effects of natural and human-induced factors on land subsidence based on multi-temporal InSAR and multi-source geospatial data: A case study in the Guangdong-Hong Kong-Macao Greater Bay Area. International Journal of Digital Earth, 16(2), 4404–4427. https://doi.org/10.1080/17538947.2023.2271882
  70. Yan, L., & Zhiping, W. (2023). Mapping the Literature on Academic Publishing: A bibliometric analysis on WOS. Sage Open, 13(1), 21582440231158562. https://doi.org/10.1177/21582440231158562
  71. Yazbeck, J., & Rundle, J. B. (2023). A Fusion of Geothermal and InSAR Data with machine learning for enhanced deformation forecasting at the geysers. Land, 12(11), 1977. https://doi.org/10.3390/land12111977
  72. Yin, W., Niu, C., Bai, Y., Zhang, L., Ma, D., Zhang, S., Zhou, X., & Xue, Y. (2023). An adaptive identification method for potential landslide hazards based on multisource data. Remote Sensing, 15(7), 1865. https://doi.org/10.3390/rs15071865
  73. Yolcu, S., Şahin, A., & Dirsehan, T. (n.d.). Gaining ground: How technology fuels hotel competitiveness-a systematic review of the literature. Tourism Planning & Development, 0(0), 1–31. https://doi.org/10.1080/21568316.2025.2534903
  74. Yu, B., Niu, T., Zhao, J., Ma, D., Liu, G., Wang, X., Dai, K., & Gu, Y. (2024). Deformation monitoring and primary driving factor analysis in the coastal area of liaohe oilfield utilizing MT-InSAR and PCA. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 92(3), 213–234. https://doi.org/10.1007/s41064-024-00283-1
  75. Yuan, R., & Chen, J. (2022). A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data. Natural Hazards, 114(2), 1393–1426. https://doi.org/10.1007/s11069-022-05430-8
  76. Yunck, T. P., Liu, C.-H., & Ware, R. (2000). A history of GPS sounding. Terrestrial, Atmospheric and Oceanic Sciences, 11(1), 001. https://doi.org/10.3319/TAO.2000.11.1.1(COSMIC)
  77. Zhang, H., Dang, X., Zhao, J., & Lu, M. (2024a). Analysis and prediction of ground deformation in Yinxi Industrial Park based on time-series InSAR technology. Environmental Monitoring and Assessment, 196(4). https://doi.org/10.1007/s10661-024-12530-4
  78. Zhang, J., Kou, P., Tao, Y., Jin, Z., Huang, Y., Cui, J., Liang, W., & Liu, R. (2024b). Urban ground subsidence monitoring and prediction using time-series InSAR and machine learning approaches: A case study of Tianjin, China. Environmental Earth Sciences, 83(16), 473. https://doi.org/10.1007/s12665-024-11778-w
  79. Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2016). Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. Journal of the Association for Information Science and Technology, 67(4), 967–972. https://doi.org/10.1002/asi.23437
  80. Zhang, L., Li, Y., & Li, R. (2023a). Driving forces analysis of urban ground deformation using satellite monitoring and multiscale geographically weighted regression. Measurement, 214, 112778. https://doi.org/10.1016/j.measurement.2023.112778
  81. Zhang, Y., Chen, Y., Ming, D., Zhu, Y., Ling, X., Zhang, X., & Lian, X. (2023b). Landslide hazard analysis based on SBAS-InSAR and MCE-CNN model: A case study of Kongtong, Pingliang. Geocarto International, 38(1), 1–22. https://doi.org/10.1080/10106049.2022.2136268
  82. Zhao, F., Gong, W., Tang, H., Pudasaini, S. P., Ren, T., & Cheng, Z. (2023). An integrated approach for risk assessment of land subsidence in Xi’an, China using optical and radar satellite images. Engineering Geology, 314, 106983. https://doi.org/10.1016/j.enggeo.2022.106983
  83. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629