Abstract
As a global geological environment problem, geological disasters are more likely to induce severe geological disasters and cause loss of personnel and property due to their suddenness and concealment, frequent heavy rainfall and regular extreme weather. In this paper, Sentinel-1A data and DEM data combined with short baseline synthetic aperture interferar (SBAS-InSAR) technology are used to analyze and calculate the spatial-temporal evolution characteristics of surface deformation in Zibo-Weifang area of Shandong Province during 2016–2019. Linear fitting of InSAR monitoring deformation results is performed using level monitoring data to verify the accuracy of the results. The findings indicate that surface deformation in most of the study area is relatively stable, but in some areas, especially near Lucun Town, the maximum negative deformation rate exceeds 80mm/a. Based on the surface deformation results, a total of 377 potential geological disasters are identified using an integrated multi-source stereo observation system of the sky and ground. This identification is done by observing optical remote sensing images and taking the absolute value of surface deformation rate greater than 10 mm/a as the judgment basis, along with ground investigation and review. Several typical geological disaster sites are screened and they are found to be in unstable state. The research results can provide scientific support for geological disaster prevention and control in Shandong Province.
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Bovenga F, Argentiero I, Refice A, Nutricato R, Nitti DO, Pasquariello G, Spilotro G (2022) Assessing the potential of long, multi-temporal SAR interferometry time series for slope instability monitoring: Two case studies in southern Italy. Remote Sensing 14(7):1677, DOI: https://doi.org/10.3390/rs14071677
Chen Y, Tao Q, Hou A, Ding L, Liu G, Wang K (2020) Accuracy verification and evaluation of Sentinel-1A repeat track differential interferometric synthetic aperture radar in monitoring mining subsidence. Journal of Applied Remote Sensing 14(1):014501, DOI: https://doi.org/10.1117/1.JRS.14.014501
Fattahi H, Amelung F (2013) DEM error correction in InSAR time series. IEEE Transactions on Geoscience and Remote Sensing 51(7):4249–4259, DOI: https://doi.org/10.1109/TGRS.2012.2227761
Ferretti A, Prati C, Rocca F (2001) Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience Remote Sensing 39(1):8–20, DOI: https://doi.org/10.1109/36.898661
Giordan D, Luzi G., Monserrat O, Dematteis N (2022) Remote sensing analysis of geologic hazards. Remote Sensing 14(19):4818, DOI: https://doi.org/10.3390/rs14194818
Gong H, Pan Y, Zheng L, Li X, Zhu L, Zhang C, Huang Z, Li Z, Wang H, Zhou C (2018) Long-term groundwater storage changes and land subsidence development in the North China Plain (1971–2015). Hydrogeology Journal 26(5):1417–1427, DOI: https://doi.org/10.1007/s10040-018-1768-4
González FAI, Santos ME, London S (2021) Persistent effects of natural disasters on human development: Quasi-experimental evidence for argentina. environment, Development and Sustainability 23(7):10432–10454, DOI: https://doi.org/10.1007/s10668-020-01064-7
Gumilar I, Abidin HZ, Hutasoit LM, Hakim DM, Sidiq TP, Andreas H (2015) Land subsidence in bandung basin and its possible caused factors. Procedia Earth and Planetary Science 12:47–62, DOI: https://doi.org/10.1016/j.proeps.2015.03.026
Guo P, Santosh M, Li S (2013) Geodynamics of gold metallogeny in the Shandong Province, NE China: An integrated geological, geophysical and geochemical perspective. Gondwana Research 24(3):1172–1202, DOI: https://doi.org/10.1016/j.gr.2013.02.004
Hooper A, Zebker HA (2007) Phase unwrapping in three dimensions with application to InSAR time series. Journal of the Optical Society of America A 24(9):2737–2747, DOI: https://doi.org/10.1364/JOSAA.24.002737
Hou J, Lv J, Chen X, Yu S (2016) China’s regional social vulnerability to geological disasters: Evaluation and spatial characteristics analysis. Natural Hazards 84:97–111, DOI: https://doi.org/10.1007/s11069-015-1931-3
Hu B, Wang H, Sun Y, Hou J, Liang J (2014) Long-term land subsidence monitoring of Beijing (China) using the small baseline subset (SBAS) technique. Remote Sensing 6(5):3648–3661, DOI: https://doi.org/10.3390/rs6053648
Huang Y, Cheng H (2013) The impact of climate change on coastal geological disasters in southeastern China. Natural Hazards 65:377–390, DOI: https://doi.org/10.1007/s11069-012-0370-7
Ikuemonisan FE, Ozebo VC, Olatinsu OB (2020) Geostatistical evaluation of spatial variability of land subsidence rates in Lagos, Nigeria. Geodesy and Geodynamics 11(5):316–327, DOI: https://doi.org/10.1016/j.geog.2020.04.001
Li Z, Cao Y, Wei J, Duan M, Wu L, Hou J, Zhu J (2019) Time-series InSAR ground deformation monitoring: Atmospheric delay modeling and estimating. Earth-Science Reviews 192:258–284, DOI: https://doi.org/10.1016/j.earscirev.2019.03.008
Li M, Zhang L, Dong J, Cai J, Liao M (2021) Detection and monitoring of potential landslides along minjiang river valley in maoxian county, sichuan using radar remote sensing. Geomatics and Information Science of Wuhan University 46(10):1529, DOI: https://doi.org/10.13203/j.whugis20210367
Liu Z, Mei G, Sun Y, Xu N (2021) Investigating mining-induced surface subsidence and potential damages based on SBAS-InSAR monitoring and GIS techniques: A case study. Environmental Earth Sciences 80(24):817, DOI: https://doi.org/10.1007/s12665-021-09726-z
Luo Q, Perissin D, Zhang Y, Jia Y (2014) L- and X-band multi-temporal InSAR analysis of Tianjin subsidence. Remote Sensing 6(9):7933–7951, DOI: https://doi.org/10.3390/rs6097933
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, DOI: https://doi.org/10.1016/j.isprsjprs.2015.10.003
Solari L, Del Soldato M, Montalti R, Bianchini S, Raspini F, Thuegaz P, Bertolo D, Tofani V, Casagli N (2019) A sentinel-1 based hot-spot analysis: Landslide mapping in north-western Italy. International Journal of Remote Sensing 40(20):7898–7921, DOI: https://doi.org/10.1080/01431161.2019.1607612
Strozzi T, Caduff R, Wegmüller U, Raetzo H, Hauser M (2017) Widespread surface subsidence measured with satellite SAR interferometry in the Swiss alpine range associated with the construction of the Gotthard Base Tunnel. Remote Sensing of Environment 190:1–12, DOI: https://doi.org/10.1016/j.rse.2016.12.007
Wang H, Jia C, Ding P, Feng K, Yang X, Zhu X (2023) Analysis and Prediction of regional land subsidence with InSAR technology and machine learning algorithm. KSCE Journal of Civil Engineering 27(2):782–793, DOI: https://doi.org/10.1007/s12205-022-1067-4
Xu Q, Dong X, Li W (2019) Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards. Geomatics and Information Science of Wuhan University 44(7):957, DOI: https://doi.org/10.13203/j.whugis20190088
Xu Y, Li T, Tang X, Zhang X, Fan H, Wang Y (2022) Research on the applicability of DInSAR, stacking-InSAR and SBAS-InSAR for mining region subsidence detection in the datong coalfield. Remote Sensing 14(14):3314, DOI: https://doi.org/10.3390/rs14143314
Xu Q, Zhang G, Li W, He C, Dong X, Guo C, Feng W (2018) Study on successive landeslide damming events of Jinsha river in Baige village on Octorber 11 and November 3, 2018 Journal of Engineering Geology 26(6):1534–1551, DOI: https://doi.org/10.13544/j.cnki.jeg.2018-406
Zhang W (2020) Geological disaster monitoring and early warning system based on big data analysis. Arabian Journal of Geosciences 13(18):946, DOI: https://doi.org/10.1007/s12517-020-05951-1
Zhang Y, Heresh F, Amelung F (2019) Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction. Computers & Geosciences 133:104331, DOI: https://doi.org/10.1016/j.cageo.2019.104331
Zhang Y, Zhou X (2017) The occurrence laws of campus stampede accidents in China and its prevention and control measures. Natural Hazards 87(2):659–673, DOI: https://doi.org/10.1007/s11069-017-2785-7
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Niu, W., Hu, X., Lin, B. et al. Detection and Monitoring of Potential Geological Disaster Using SBAS-InSAR Technology. KSCE J Civ Eng 27, 4884–4896 (2023). https://doi.org/10.1007/s12205-023-0759-8
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DOI: https://doi.org/10.1007/s12205-023-0759-8