Open Access Open Access  Restricted Access Subscription Access

Monitoring of Land Deformation in Kanagawa Prefecture Using GNSS and C-band Sentinel-1 Based Consecutive DInSAR

katsunoshin nishi

Abstract


In Japan, precipitation has recently become more localized, concentrated, and intense. Extreme precipitation has caused a rapid land deformation, which results in ground water levels and loss of building stability. For the appropriate maintenance and renewal of infrastructure, including the design method of underground structures, the utilization of remote sensing technology which can always observe the ground deformation over a wide range with high accuracy is expected. This research presents the results of long-term continuous land deformation (subsidence or uplift) monitoring and using Global Navigation Satellite System (GNSS) and Consecutive Differential Interferometric Synthetic Aperture Radar (DInSAR) technique in Yokohama, Yokosuka and Miura cities, Kanagawa Prefecture, Japan. We adopted the Consecutive DInSAR method with the small spatial and short time baseline between satellites. The satellite data used thirty-three pairs of Sentinel-1 data in descending orbit from December 27, 2017 to November 17, 2020, and twenty-two Sentinel-1 data pairs in ascending orbit from June 26, 2019 to October 30, 2020. The validation of the SAR images used the daily coordinate values of the GNSS control point provided by the Geographical Survey Institute (GSI). As a result of the analysis, the estimated land displacement and GNSS data agreed well in Yokohama, Yokosuka, and Miura cities. The RMSE in descending orbit of Yokohama, Yokosuka and Miura cities were found to be 0.41 cm, 0.49 cm, and 0.49 cm, respectively and the RMSE in ascending orbit of Yokohama, Yokosuka and Miura cities were found to be 0.68 cm, 0.53 cm, and 0.58 cm, respectively, demonstrating its capability to analyze the land displacement monitoring over under different topographical conditions. In the future, this research result is expected to applicable in the field of disaster monitoring and environmental change observation which continuously require high accuracy observation over a wide area.

Keywords


Consecutive DInSAR; GNSS; Sentinel-1; Land Deformation; Kanagawa Prefecture.

Full Text:

PDF

References


The National Institute of Population and Social Security Research. Results of age-specific population projection (total, 0–14, 15–64, 65+, and 75+) for 2015–2045; The National Institute of Population and Social Security Research, Tokyo, Japan, 2018.

The Ministry of Land, Infrastructure, Transport and Tourism. National Spatial Strategy; The Ministry of Land, Infrastructure, Transport and Tourism, Tokyo, Japan, 2015.

T. Kisanuki, M. Nishigaki, S.Noda, and T. Yamashita,“A Method of Determining The Groundwater Level for Design of Underground Stracture in The Geological Region Around Tokyo,” J. JSCE, vol. 57, pp. 167–176, 2002.

T. Kusaka et al, “Study on ground upheaval caused by the rise in groundwater level by centrifuge tests,” Japanese Geotechnical Journal, vol. 6, no. 3, pp. 439–454, 2011.

J. T. Sri Sumantyo, M. Shimada, P. P. Mathieu, and H. Z. Abidin, “Long-term consecutive DInSAR for volume change estimation of Land deformation,” IEEE Trans Geosci Remote Sens., vol. 50, no. 1, pp. 259–270, 2012.

P. Razi, J. T. S. Sumantyo, D. Perissin, H. Kuze, M. Y. Chua, and G. F. Panggabean, “3D land mapping and land deformation monitoring using persistent scatterer interferometry (PSI) ALOS PALSAR: Validated by Geodetic GPS and UAV,” IEEE Access, vol. 6, pp. 12395–12404, 2018.

A. Ferretti, C. Prati, and F. Rocca, “Permanent scatterers in SAR interferometry,” IEEE Trans Geosci Remote Sens, vol. 39, no. 1, pp. 8–20, 2001.

P. Berardino, G. Fornaro, R. Lanari, and E. Sansosti, “A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms,” IEEE Trans Geosci Remote Sens, vol. 40, no. 11, pp. 2375–2383, 2002.

T. Kobayashi.; Y. Morishita.; and S. Yamada. A Prototype System for InSAR time series analysis; The Geographical Survey Institute, Tokyo, Japan, 2018; pp.123-133.

J. Widodo et al., “Land subsidence rate analysis of Jakarta Metropolitan Region based on D-InSAR processing of Sentinel data C-Band frequency,” J. Phys. Conf. Ser, vol. 1185, no. 1, 2019.

H. Munekane. On Improving Precision of GPS-derived Height Time Series at GEONET Stations; The Geographical Survey Institute, Tokyo, Japan, 2013; pp.39-46.

T. ElGharbawi and M. Tamura, “Measuring deformations using SAR interferometry and GPS observables with geodetic accuracy: Application to Tokyo, Japan,” ISPRS J. Photogramm. Remote Sens, vol. 88, pp. 156–165, 2014.

H. Nakagawa, T. Toyofuku, and K. Kotani, “Development and validation of GEONET new analysis strategy (version 4),” J. Geogr. Surv. Inst, vol. 118, pp. 1–8, 2009.

Crustal Movement Reevaluated from Solutions of GEONET New Analysis Strategy (Ver.4):

Available online: https://www.gsi.go.jp/common/000054720.pdf (accessed on April 2, 2020).

Geospatial Information Authority of Japan: Causes and characteristics of coordinates changes other than crustal movement.

Available online: https://www.gsi.go.jp/top.html (accessed on November 17, 2020).

T. Mikasa, T. Uchihara, T. Shigemura, T.Tanaka and Y. Yamazaki, “A Study on Topographical Characteristic and Land Use Feature in ‘Yato,’” J Archit Plann Res, vol. 80, no. 714, pp. 1825–1832, 2015.

The Geographical Survey Institute: Elevation of observation point. Available online: https://www.gsi.go.jp/johofukyu/hyoko_system.html. (accessed on September 17, 2020).

Geology view of Yokohama city. Available online: https://wwwm.city.yokohama.lg.jp/yokohama/Portal (accessed on September 17, 2020).

The Geological Survey of Japan: Geology of Yokosuka city area:

Available online: https://www.gsj.jp/data/50KGM/PDF/GSJ_MAP_G050_08084_1998_D.pdf (accessed on November 17, 2020).

Geology view of Miura city. Available online: http://www.kanagawa-boring.jp/boring/index.htm(accessed on November 17, 2020).

The Geological Survey of Japan: Geology map of Kanagawa prefecture. Available online: https://www.gsj.jp/en/ (accessed on November 17, 2020).

Japan meteological agency: Precipitation information of Kanagawa prefecture: Available online: https://www.jma.go.jp/jma/indexe.html. (accessed on November 17, 2020).

J. Widodo et al., “Application of SAR interferometry using ALOS-2 PALSAR-2 data as precise method to identify degraded peatland areas related to forest fire,” Geosci, vol. 9, no. 11, pp. 1–15, 2019.

M. Yamanaka, Y. Morishita, and Y. Osaka, “Detection of ground subsidence by InSAR time series analysis,” J. Geod. Soc. Japan, vol. 49, no. 1, pp. 1–23, 2003.

A. Hasegawa, J. Nakajima, N. Uchida, and N. Umino, “Subduction of Two Oceanic Plates and Unique Seismic Activity beneath the Tokyo Metropolitan Area,” J Geog , vol. 122, no. 3, pp. 398–417, 2013.

Geospatial Information Authority of Japan: Error of the interferometric SAR. Available online: https://www.gsi.go.jp/uchusokuchi/sar_error.html.(accessed on December 29, 2020).

P. Razi, J. T. S. Sumantyo, K. Nishi, J. Widodo, A. Munir, and F. Febriany, “Effect of Earthquake Intensity to Land Deformation Observed from Space,” Prog. Electromagn. Res. Symp., vol. 2019-June, pp. 2123–2128, 2019.

A. Moreira, P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, and K. P. Papathanassiou, “A tutorial on synthetic aperture radar,” IEEE Trans.Geosci. Remote Sens, vol. 1, no. 1, pp. 6–43, 2013.

R. F. Putri, L. Bayuaji, J. T. S. Sumantyo, and H. Kuze, “Terrasar-X DInSAR for land deformation detection in Jakarta Urban area, Indonesia,” J. Urban Environ. Eng., vol. 7, no. 2, pp. 195–205, 2013.

Geology view of Yokosuka city. Available online: https://www.gsi.go.jp/johofukyu/hyoko_system.html. (accessed on September 17, 2020).

K. Ishitsuka, T. Matsuoka, “Accuracy Evaluation of Persistent Scatterer Interferometry Using ALOS/PALSAR Data:A Case Study of Surface Displacement in the Kujukuri Plain, Chiba Prefecture,” J. Remote Sens. Soc. Japan, vol. 36, no. 4, pp. 328–337, 2016.

H. Iwaki et al. Deformation and analysis and trial study for structural monitoring using C-band SAR imaging satellite for 2016 Kumamoto. J. JSCE, vol. 73, no. 4, p. I_1018-I_1023, 2017.

Y. Morishita. Reduction of Spatially Long Wavelength Noises in SAR Interferograms Using GNSS Data. Journal of the Geodetic Society of Japan, vol. 62, no. 2, pp. 89–100, 2016.

J. Tetuko et al., “Analysis of Coastal Sedimentation Impact to Jakarta Giant Sea Wall Using PSI ALOS PALSAR,” IEEE Geosci. Remote Sens. Lett., vol. 13, no. 10, pp. 1472–1476, 2016.

Katsunoshin Nishi, J. T. S. Sumantyo, Mirza Muhammad Waqar, Chen Xiangping, Ramadan Gamal, “Accuracy Verification of Consecutive DInSAR and PSI-SAR Using GNSS Data” IEICE Technical Report, vol.120, no. 250, pp.13-18, 2020.




DOI: http://dx.doi.org/10.21535%2Fjias.v7i1.1043

Refbacks

  • There are currently no refbacks.




Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.