Monitoring and spatiotemporal evolution of land surface subsidence in Jiutai District,Changchun City based on InSAR
Coal mining is one of the main factors causing land subsidence,which is destructive to land resources,and seriously affects the living environment of mining residents and the development of regional agriculture.Therefore,it is necessary to monitor the surface subsidence in the surrounding area of mining area.148 Sentinel-1B images of Jiutai District of Changchun were obtained from January 2017 to December 2021.Based on Small Baseline Subset InSAR(SBAS-InSAR),the surface subsidence of Jiutai District of Changchun was monitored.The results of surface subsidence rate and cumulative subsidence were obtained,and the reliability of monitoring results was verified by precision and field investigation.The spatio-temporal evolution of surface subsidence in Jiutai District of Changchun City was analyzed.The results showed that during the monitoring period from 2017 to 2021,serious land subsidence occurred in Jiutai District of Changchun City,mainly concentrated in the northwest of Yingcheng City and Yangcaogou area.Its spatial distribution and spatio-temporal evolution were highly correlated,and the sedimentation area S3,located in the middle of Yangcaogou Village and Yaozhan Village,was the fastest in the monitoring area.The subsidence area was about 11.59 km2,and the maximum annual vertical subsidence rate and subsidence area was the largest among the three subsidence zones.The settlement showed a trend of continuous increase,and it took a long time to restore the relative stability of the surface.The study shows the feasibility of SBAS-InSAR technology in monitoring ground subsidence in underground coal mining area,which can provide scientific reference for the prevention and control of geological disasters in mining areas and the restoration and treatment of geological environment in mining areas.
SBAS-InSARsurface subsidenceSpatiotemporal evolution characteristicsJiutai District,Changchun City
NETL
NSTL
万方数据
