Research on Land-Based Wind/Solar Power Station Site Deformation Monitoring Based on SBAS-InSAR Technology

Junke Guo; Ling Liu; Yongfeng Zheng; Wei Cai; Zhijun Wang; Shangqi Wang

doi:10.4108/ew.5656

Authors

Junke Guo China Energy Engineering Group
Ling Liu China Energy Engineering Group
Yongfeng Zheng China Energy Engineering Group
Wei Cai China Energy Engineering Group
Zhijun Wang China Energy Engineering Group
Shangqi Wang China Energy Engineering Group

DOI:

https://doi.org/10.4108/ew.5656

Keywords:

LAnd-based Wind/Solar Power Station, Deformation Monitoring, Time Series, SBAS-InSAR

Abstract

INTRODUCTION: In recent years, China has been building extensive wind/solar power stations. During the construction and operation of land-based wind/solar power stations, deformation monitoring is an important method to investigate the station stability.

OBJECTIVES: Therefore, this study uses Sentinel-1 data and time-series InSAR technology to monitor the deformation of photovoltaic and wind power stations in Qingyuan County.

METHODS: InSAR technology obtains deformation rate maps in the radar line of sight (LOS) direction for a wide area around the power station sites. Since wind/solar power stations are mainly located in natural environments with relatively dense vegetation coverage, this paper proposes a SBAS-InSAR method integrated with spatiotemporal filtering to accurately extract the time series deformation over a large area. Based on the statistical characteristic difference between the deformation and the atmospheric delay, spatiotemporal filterings are applied to remove the atmospheric delay from the InSAR derived deformation results.

RESULTS: The experimental results show that spatiotemporal filtering is an effective and fast method to remove atmospheric delay.

CONCLUSION: The integration of BSAS-InSAR with spatiotemporal filtering has great potential applications in the deformation monitoring of land-based wind/solar power station sites, which is critical for the construction and operation of land-based wind/solar power stations.

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References

Wang, Y., Chao, Q., Zhao, L., & Chang, R.. Assessment of wind and photovoltaic power potential in China. Carbon Neutrality. 2022; 1(1), 15. DOI: https://doi.org/10.1007/s43979-022-00020-w

Lin, C. K., Dai, C. Y., & Wu, J. C. Analysis of structural deformation and deformation-induced solar radiation misalignment in a tracking photovoltaic system. Renewable energy. 2013; 59, 65-74. DOI: https://doi.org/10.1016/j.renene.2013.03.031

Abdollahi, R. Impact of wind on strength and deformation of solar photovoltaic modules. Environmental Science and Pollution Research. 2021; 28(17), 21589-21598. DOI: https://doi.org/10.1007/s11356-020-12111-1

Raucoules, D., et al., Validation and intercomparison of Persistent Scatterers Interferometry: PSIC4 project results. Journal of Applied Geophysics, 2009. 68(3): p. 335-347. DOI: https://doi.org/10.1016/j.jappgeo.2009.02.003

Cigna, F., Osmanoglu, B., Cabral-Cano, E.. Dixon, T.H, Avila-Olivera, J.A., Garduno-Monroy, V.H., DeMets, C., Wdowinski, S. Monitoring land subsidence and its induced geological hazard with synthetic aperture radar interferometry: A case study in Morelia, Mexico. Remote Sens. Environ. 2012;117,146-161. DOI: https://doi.org/10.1016/j.rse.2011.09.005

Costantini, M., Ferretti, A., Minati, F., Falco, S., Trillo, F., Colombo, D., Novali, F..Malvarosa, F., Mammone, C., Vecchioli, F., Rucci, A., Fumagalli, A., Allievi, J..Ciminelli. M.G.. Costabile. S. Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-Sky Medradar data. Remote Sens. Environ. 2017; 202,250-275. DOI: https://doi.org/10.1016/j.rse.2017.07.017

Osmanoğlu, B., Sunar, F., Wdowinski, S., Cabral-Cano, E. Time series analysis of InSAR data: Methods and trends. ISPRS J. Photogramm. Remote Sens. 2016; 115, 90-102. DOI: https://doi.org/10.1016/j.isprsjprs.2015.10.003

Massonnet, D., Rossi, M., Carmona, C., Adragna, F., Peltzer, G., Feigl, K., Rabaute, T. The displacement field of the Landers earthquake mapped by radar interferometry. Nature. 1993; 364 (6433), 138–142. DOI: https://doi.org/10.1038/364138a0

Wasowski, J., Bovenga, F. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Eng. Geol. 2014; 174, 103-138. DOI: https://doi.org/10.1016/j.enggeo.2014.03.003

Crosetto, M., Monserrat, O., Bremmer, C., Hanssen, R., Capes, R., Marsh, S.Ground motion monitoring using SAR interferometry: quality assessment. Eur.Geol. 2009; 26, 12–15.

Colesanti, C., & Wasowski, J. Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Engineering geology. 2006; 88(3-4), 173-199. DOI: https://doi.org/10.1016/j.enggeo.2006.09.013

Berardino, P., Costantini, M., Franceschetti, G., Iodice, A., Pietranera, L., & Rizzo, V. Use of differential SAR interferometry in monitoring and modelling large slope instability at Maratea (Basilicata, Italy). Engineering Geology. 2003; 68(1-2), 31-51. DOI: https://doi.org/10.1016/S0013-7952(02)00197-7

Lanari, R., Mora, O., Manunta, M., Mallorquí, J. J., Berardino, P., & Sansosti, E. A small-baseline approach for investigating deformations on full-resolution differential SAR interferograms. IEEE transactions on geoscience and remote sensing. 2004; 42(7), 1377-1386. DOI: https://doi.org/10.1109/TGRS.2004.828196

Casu, F., Manzo, M., & Lanari, R. A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data. Remote Sensing of Environment. 2006; 102(3-4), 195-210. DOI: https://doi.org/10.1016/j.rse.2006.01.023

Ferretti, A., Prati, C., & Rocca, F. Permanent scatterers in SAR interferometry. IEEE Transactions on geoscience and remote sensing. 2001; 39(1), 8-20. DOI: https://doi.org/10.1109/36.898661

Hooper, A., Zebker, H., Segall, P., & Kampes, B. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophysical research letters; 2004; 31(23). DOI: https://doi.org/10.1029/2004GL021737

Hu, X., Biirgmann, R., Schulz, W.H., Fielding, E.J. Four-dimensional surface motions of the Slumgullion landslide and quantification of hydrometeorological forcing. Nat.Commun. 2020; 11,2792. DOI: https://doi.org/10.1038/s41467-020-16617-7

Huang, O., Crosetto, M., Monserrat, 0., Crippa, B. Displacement monitoring and modelling of a high-speed railway bridge using C-band Sentinel-1 data. ISPRS J.Photogramm.Remote Sens. 2017; 128204-211. DOI: https://doi.org/10.1016/j.isprsjprs.2017.03.016

Liu, X., Zhao, C., Zhang, Q., Lu, Z., Li, Z., Yang, C., ... & Liu, C. Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China. Eng. Geol. 2021; 284, 106033. DOI: https://doi.org/10.1016/j.enggeo.2021.106033

Li, Z., Muller, J. P., Cross, P., Fielding, E. J. Interferometric synthetic aperture radar (InSAR) atmospheric correction: GPS, Moderate Resolution Imaging Spectroradiometer (MODIS), and InSAR integration. J. Geophys. Res. Solid Earth. 2005; 110(B3). DOI: https://doi.org/10.1029/2004JB003446

Doin, M.P., Lasserre, C., Peltzer, G., Cavalié, O., Doubre, C. Corrections of stratified tropospheric delays in SAR interferometry: validation with global atmospheric models. J. Appl. Geophys. 2009; 69, 35–50. DOI: https://doi.org/10.1016/j.jappgeo.2009.03.010

Jolivet, R., Grandin, R., Lasserre, C., Doin, M.P., Peltzer, G. Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data. Geophys. Res. Lett. 2011; 38. DOI: https://doi.org/10.1029/2011GL048757

Beauducel, F., Briole, P., Froger, J.-L. Volcano-wide fringes in ERS synthetic aperture radar interferograms of Etna (1992–1998): deformation or tropospheric effect? Journal of Geophysical Research: Solid Earth. 2000; 105, 16391–16402. DOI: https://doi.org/10.1029/2000JB900095

Dong, J., Zhang, L., Liao, M., & Gong, J. Improved correction of seasonal tropospheric delay in InSAR observations for landslide deformation monitoring. Remote Sensing of Environment. 2019; 233, 111370. DOI: https://doi.org/10.1016/j.rse.2019.111370

Werner, C., Wegmuller, U., Strozzi, T., Wiesmann, A. Interferometric point target analysis for deformation mapping. Geoscience and Remote Sensing Symposium, 2003. IGARSS’03. Proceedings. DOI: https://doi.org/10.1142/9789812702630_0015

Cai, J., Zhang, L., Dong, J., Guo, J., Wang, Y., & Liao, M. Automatic identification of active landslides over wide areas from time-series InSAR measurements using Faster RCNN. International Journal of Applied Earth Observation and Geoinformation. 2023; 124, 103516. DOI: https://doi.org/10.1016/j.jag.2023.103516