Automated processing based on interferometer technique with permanent dispersers for subsidence monitoring (Case study of Herat and Marvast aquifers)
Subject Areas : Applications in natural hazard and disaster
Abolfazl Mohammadi Fatehabad
1
(
MSc. Student of Remote Sensing and Geographical Information System, Faculty of Engineering, Islamic Azad University of Yazd, Yazd, Iran
)
Seyed Ali Alhoseini Almodaresi
2
(
Professor, Department of Remote Sensing, Faculty of Engineering, Islamic Azad University of Yazd, Yazd, Iran
)
Keywords: Permanent scatterers interferometry (PSI), Herat and Marvast aquifers, Land subsidence, Sentinel-1 satellite, Differential interferometry (DInSAR),
Abstract :
Background and Objective One of the issues that occur due to groundwater abstraction is land subsidence. This situation is now reported in many arid and semi-arid regions of Iran, especially in Yazd province. In addition, in recent decades, heterogeneous development of agricultural lands and uncontrolled extraction of groundwater from the reservoirs of Herat and Marvast aquifers in Yazd province have caused the occurrence of land subsidence around agricultural lands. The rising metal wall of deep wells and the horizontal cracks on the ground directly indicate the degree of subsidence. It is necessary to identify and identify areas that are prone to subsidence due to the risk and danger to life. On the other hand, we must note that The effects of subsidence may be accelerated by other natural activities in the area such as volcanic activity, earthquakes and landslides, and due to the seismicity of many areas of our country, attention to this phenomenon is of particular importance. Today, one of the most accurate and cost-effective methods for detecting ground surface movements is the radar interference technique. By comparing the phases of two radar images taken from the same area at two different times, this method will be able to determine changes in the earth's surface with accuracy and spatial resolution in centimetres and even millimetres in that time interval. In this article, for the first time, we tried to monitor the subsidence of land subsidence in Herat and Marva's aquifers by using Sentinel-1 satellite images and open source software. In this research, we try to achieve the following goals by using the time series data of the Sentinel-1 sensor, which has not been used in the study of subsidence of the studied areas. The aim of this study is to implement the technique of interferometry with permanent distributors using the integrated SNAP2StaMPS package. Another goal can be to estimate the subsidence rate by processing a set of Sentinel-1 sensor images in the period 20/02/2017 to 10/02/2019, approximately two years of time series. The ultimate goal was to investigate the data potential of this sensor in time series analysis and monitoring of changes due to land subsidence.Materials and Methods Herat and Marvast aquifers, in fact, the study areas in this study include Herat and Marvast aquifers located in Yazd province. Herat and Marvast aquifers are geologically part of the Zagros (ophiolite, radiolarite) and Sanandaj-Sirjan zones. The study areas are located in the 2nd-degree catchment area of Abargoo and Sirjan deserts with code 44 and an area equal to 57125 square kilometres. In this study, 24 data related to Sentinel-1 sensor were processed in one-dimensional mixed image level, high pass, VV polarization and segment number 93 related over a period of approximately two years in both study areas. In general, most of the process of processing and analyzing the time series of interferometers with permanent distributors (PSI) in this paper was done by two open source software, Snap and Stamps. Finally, to automate the single-reference interferometry steps, a set of code written in the Python programming language called SNAP2StaMPS was used, which is well designed based on the graphs of the Snap software.Results and Discussion One of the results of interferometric processing based on the innovative SNAP2StaMPS algorithm in this research was the production of interference maps from which the topographic phase has been omitted. Other results of the standard deviation scatter index for the average displacement map of both Herat and Marva's aquifers were 4.19 and 3.65 mm per year, respectively. Also, the main results of this study are the estimation of the average displacement map of the Herat aquifer between -40.33 to 11.46 mm per year and for Marva's aquifer between-39.79 to 10.63 mm per year in terms of satellite visibility during the study period (2017 to 2019). For this purpose, areas were randomly selected and areas based on subsidence field evidence in both study areas were selected. Hajiabad Naseri and, 6) Marvast city, related to the Marvast aquifer can be named. In this paper, due to the lack of specialized tools to evaluate and validate the only way to review the results, its compliance with ground subsidence evidence, time series diagrams and hydrograph of the aquifer unit. According to the hydrograph results of Herat and Marvast alluvial aquifers, the groundwater level in the Herat aquifer has decreased by about 5.5 meters during the 8-year period from 2011 to 2019, based on data from 28 observation wells. This hydrograph shows a drop of groundwater of about 7 meters over an eight-year period. The time series results obtained from the interferometry of the images used in this paper show the slope of the fitting line, which actually shows the amount of displacement (up or down), indicating a downward trend in the second area (white aqueduct of Herat aquifer) and Fifth (Shuran aqueduct from Marvast aquifer) shows its amount equal to about 5 and 7 cm, respectively. These results have a significant relationship with the hydrograph of the unit of both aquifers.Conclusion In this study, for the first time, to estimate the rate of subsidence in Herat and Marvast aquifers in Yazd province, the technique of interferometry with permanent dispersants was used using Sentinel-1 sensor data and SNAP2StaMPS open source package. Also, the potential of Stamps and SNAP software for radar interferometry processing was investigated, and also the details of the implementation of the Step to Stamps software package were shown. In general, based on the processed outputs of this package and the results of validation, it is possible to understand the ability of the automated method presented in this study to monitor subsidence and use this algorithm in other study areas.
Arvin A, Vahabzadeh G, Mousavi SR, Bakhtyari Kia M. 2019. Geospatial modeling of land subsidence in the south of the Minab watershed using remote sensing and GIS. Journal of RS and GIS for Natural Resources, 10(3): 19-34. (In Persian).
Bamler R, Hartl P. 1998. Synthetic aperture radar interferometry. Inverse problems, 14(4): R1-R54. doi:http://dx.doi.org/10.1088/0266-5611/14/4/001.
Berardino P, Fornaro G, Lanari R, Sansosti E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on geoscience and remote sensing, 40(11): 2375-2383. doi:http://dx.doi.org/10.1109/TGRS.2002.803792.
Bozzano F, Esposito C, Franchi S, Mazzanti P, Perissin D, Rocca A, Romano E. 2015// 2015. Analysis of a Subsidence Process by Integrating Geological and Hydrogeological Modelling with Satellite InSAR Data. In: Lollino G, Manconi A, Guzzetti F, Culshaw M, Bobrowsky P, Luino F (eds) Engineering Geology for Society and Territory - Volume 5, Cham. Springer International Publishing, pp 155-159.
Bozzano F, Esposito C, Franchi S, Mazzanti P, Perissin D, Rocca A, Romano E. 2015// 2015. Analysis of a Subsidence Process by Integrating Geological and Hydrogeological Modelling with Satellite InSAR Data. In: Lollino G, Manconi A, Guzzetti F, Culshaw M, Bobrowsky P, Luino F (eds) Engineering Geology for Society and Territory - Volume 5, Cham. Springer International Publishing, pp 155-159. https://doi.org/110.1007/1978-1003-1319-09048-09041_09031.
Cian F, Blasco JMD, Carrera L. 2019. Sentinel-1 for monitoring land subsidence of coastal cities in Africa using PSInSAR: a methodology based on the integration of SNAP and StaMPS. Geosciences, 9(3): 124. doi:https://doi.org/10.3390/geosciences9030124.
Crosetto M, Monserrat O, Cuevas-González M, Devanthéry N, Crippa B. 2016. Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115: 78-89. doi:https://doi.org/10.1016/j.isprsjprs.2015.10.011.
Dehghani M. 2016. Landslide Monitoring Using Hybrid Conventional and Persistent Scatterer Interferometry. Journal of the Indian Society of Remote Sensing, 44(4): 505-513. doi:https://doi.org/10.1007/s12524-015-0536-3.
Delgado Blasco J, Foumelis M. Automated SNAP Sentinel-1 DInSAR processing for StaMPS PSI with open source tools (version 1.0. 1). Zenodo Available online: doi:http://doiorg/105281/zenodo1322353.
Delgado Blasco JM, Foumelis M, Stewart C, Hooper A. 2019. Measuring urban subsidence in the Rome metropolitan area (Italy) with Sentinel-1 SNAP-StaMPS persistent scatterer interferometry. Remote Sensing, 11(2): 129. doi:https://doi.org/10.3390/rs11020129.
Ferretti A, Prati C, Rocca F. 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on geoscience and remote sensing, 38(5): 2202-2212. doi:http://dx.doi.org/10.1109/36.868878.
Ferretti A, Prati C, Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote sensing, 39(1): 8-20. doi:http://dx.doi.org/10.1109/36.898661
FerrettiA M, GuarnieriA P. 2007. InSAR Principles: GuidelinesforSARInterferometryProcessingand Interpretation. ESAPublications.
Foroughnia F, Nemati S, Maghsoudi Y. 2018. PS-InSAR Time Series Analysis Using Sentinel-1A and ENVISAT-ASAR Data Stacks for Subsidence Estimation in Tehran. Iranian Journal of Remote Sensing & GIS, 10(1): 57-72.
Foumelis M, Blasco JMD, Desnos Y-L, Engdahl M, Fernández D, Veci L, Lu J, Wong C. 2018. ESA SNAP-StaMPS integrated processing for sentinel-1 persistent scatterer interferometry. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp 1364-1367.
Galloway D, Burbey T. 2011. Review: regional land subsidence accompanying groundwater extraction. Hydrogeology 19: 1459–1486. doi:https://doi.org/10.1007/s10040-011-0775-5
Hooper A, Bekaert D, Spaans K, Arıkan M. 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics, 514-517: 1-13. doi:https://doi.org/10.1016/j.tecto.2011.10.013.
Hooper A, Spaans K, Bekaert D, Cuenca MC, Arıkan M, Oyen A. 2010. StaMPS/MTI manual. Delft Institute of Earth Observation and Space Systems Delft University of Technology, Kluyverweg, 1: 2629.
Jelének J, Kopačková V, Fárová K. 2018. Post-earthquake landslide distribution assessment using sentinel-1 and-2 data: The example of the 2016 mw 7.8 earthquake in New Zealand. In: Proceedings, vol 7. MDPI AG, pp 361. doi:https://doi.org/310.3390/ecrs-3392-05174.
Mahapatra P, der Marel Hv, van Leijen F, Samiei-Esfahany S, Klees R, Hanssen R. 2018. InSAR datum connection using GNSS-augmented radar transponders. Journal of Geodesy, 92(1): 21-32. doi:https://doi.org/10.1007/s00190-017-1041-y.
Mancini F, Grassi F, Cenni N. 2021. A workflow based on SNAP–StaMPS open-source tools and GNSS data for PSI-Based ground deformation using dual-orbit sentinel-1 data: Accuracy assessment with error propagation analysis. Remote Sensing, 13(4): 753.
Orellana F, Delgado Blasco JM, Foumelis M, D’Aranno PJ, Marsella MA, Di Mascio P. 2020. Dinsar for road infrastructure monitoring: Case study highway network of Rome metropolitan (Italy). Remote Sensing, 12(22): 3697. doi:https://doi.org/10.3390/rs12223697.
Scheiber R, Moreira A. 2000. Coregistration of interferometric SAR images using spectral diversity. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2179-2191. doi:https://doi.org/10.1109/36.868876.
Takaku J, Tadono T, Tsutsui K, Ichikawa M. 2016. Validation of" AW3D" global DSM generated from Alos Prism. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3: 25. doi:http://dx.doi.org/10.5194/isprs-annals-III-4-25-2016.
Villasenor J, Zebker H. 1992. Temporal decorrelation in repeat pass-radar interferometry. In: In: IGARSS'92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 2 (A93-47551 20-43). Institute of Electrical and Electronics Engineers, Inc.
Zan De F, Guarnieri AM. 2006. TOPSAR: Terrain observation by progressive scans. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2352-2360. doi:http://dx.doi.org/10.1109/TGRS.2006.873853.
Zarekamali M, Alhoseini Almodaresi SA, Naghdi K. 2017. Comparing the magnitude of the earth’s vertical relocation using the SBAS algorithm in X and C radar bands (Case study: Tehran lands). Journal of RS and GIS for Natural Resources, 8(3): 104-120. (In Persian).
Zhou X, Chang N-B, Li S. 2009. Applications of SAR interferometry in earth and environmental science research. Sensors, 9(3): 1876-1912. doi: https://doi.org/10.3390/s90301876.
Zhu XX, Wang Y, Montazeri S, Ge N. 2018. A review of ten-year advances of multi-baseline SAR interferometry using TerraSAR-X data. Remote Sensing, 10(9): 1374. doi:https://doi.org/10.3390/rs10091374.
_||_Arvin A, Vahabzadeh G, Mousavi SR, Bakhtyari Kia M. 2019. Geospatial modeling of land subsidence in the south of the Minab watershed using remote sensing and GIS. Journal of RS and GIS for Natural Resources, 10(3): 19-34. (In Persian).
Bamler R, Hartl P. 1998. Synthetic aperture radar interferometry. Inverse problems, 14(4): R1-R54. doi:http://dx.doi.org/10.1088/0266-5611/14/4/001.
Berardino P, Fornaro G, Lanari R, Sansosti E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on geoscience and remote sensing, 40(11): 2375-2383. doi:http://dx.doi.org/10.1109/TGRS.2002.803792.
Bozzano F, Esposito C, Franchi S, Mazzanti P, Perissin D, Rocca A, Romano E. 2015// 2015. Analysis of a Subsidence Process by Integrating Geological and Hydrogeological Modelling with Satellite InSAR Data. In: Lollino G, Manconi A, Guzzetti F, Culshaw M, Bobrowsky P, Luino F (eds) Engineering Geology for Society and Territory - Volume 5, Cham. Springer International Publishing, pp 155-159.
Bozzano F, Esposito C, Franchi S, Mazzanti P, Perissin D, Rocca A, Romano E. 2015// 2015. Analysis of a Subsidence Process by Integrating Geological and Hydrogeological Modelling with Satellite InSAR Data. In: Lollino G, Manconi A, Guzzetti F, Culshaw M, Bobrowsky P, Luino F (eds) Engineering Geology for Society and Territory - Volume 5, Cham. Springer International Publishing, pp 155-159. https://doi.org/110.1007/1978-1003-1319-09048-09041_09031.
Cian F, Blasco JMD, Carrera L. 2019. Sentinel-1 for monitoring land subsidence of coastal cities in Africa using PSInSAR: a methodology based on the integration of SNAP and StaMPS. Geosciences, 9(3): 124. doi:https://doi.org/10.3390/geosciences9030124.
Crosetto M, Monserrat O, Cuevas-González M, Devanthéry N, Crippa B. 2016. Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115: 78-89. doi:https://doi.org/10.1016/j.isprsjprs.2015.10.011.
Dehghani M. 2016. Landslide Monitoring Using Hybrid Conventional and Persistent Scatterer Interferometry. Journal of the Indian Society of Remote Sensing, 44(4): 505-513. doi:https://doi.org/10.1007/s12524-015-0536-3.
Delgado Blasco J, Foumelis M. Automated SNAP Sentinel-1 DInSAR processing for StaMPS PSI with open source tools (version 1.0. 1). Zenodo Available online: doi:http://doiorg/105281/zenodo1322353.
Delgado Blasco JM, Foumelis M, Stewart C, Hooper A. 2019. Measuring urban subsidence in the Rome metropolitan area (Italy) with Sentinel-1 SNAP-StaMPS persistent scatterer interferometry. Remote Sensing, 11(2): 129. doi:https://doi.org/10.3390/rs11020129.
Ferretti A, Prati C, Rocca F. 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on geoscience and remote sensing, 38(5): 2202-2212. doi:http://dx.doi.org/10.1109/36.868878.
Ferretti A, Prati C, Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote sensing, 39(1): 8-20. doi:http://dx.doi.org/10.1109/36.898661
FerrettiA M, GuarnieriA P. 2007. InSAR Principles: GuidelinesforSARInterferometryProcessingand Interpretation. ESAPublications.
Foroughnia F, Nemati S, Maghsoudi Y. 2018. PS-InSAR Time Series Analysis Using Sentinel-1A and ENVISAT-ASAR Data Stacks for Subsidence Estimation in Tehran. Iranian Journal of Remote Sensing & GIS, 10(1): 57-72.
Foumelis M, Blasco JMD, Desnos Y-L, Engdahl M, Fernández D, Veci L, Lu J, Wong C. 2018. ESA SNAP-StaMPS integrated processing for sentinel-1 persistent scatterer interferometry. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp 1364-1367.
Galloway D, Burbey T. 2011. Review: regional land subsidence accompanying groundwater extraction. Hydrogeology 19: 1459–1486. doi:https://doi.org/10.1007/s10040-011-0775-5
Hooper A, Bekaert D, Spaans K, Arıkan M. 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics, 514-517: 1-13. doi:https://doi.org/10.1016/j.tecto.2011.10.013.
Hooper A, Spaans K, Bekaert D, Cuenca MC, Arıkan M, Oyen A. 2010. StaMPS/MTI manual. Delft Institute of Earth Observation and Space Systems Delft University of Technology, Kluyverweg, 1: 2629.
Jelének J, Kopačková V, Fárová K. 2018. Post-earthquake landslide distribution assessment using sentinel-1 and-2 data: The example of the 2016 mw 7.8 earthquake in New Zealand. In: Proceedings, vol 7. MDPI AG, pp 361. doi:https://doi.org/310.3390/ecrs-3392-05174.
Mahapatra P, der Marel Hv, van Leijen F, Samiei-Esfahany S, Klees R, Hanssen R. 2018. InSAR datum connection using GNSS-augmented radar transponders. Journal of Geodesy, 92(1): 21-32. doi:https://doi.org/10.1007/s00190-017-1041-y.
Mancini F, Grassi F, Cenni N. 2021. A workflow based on SNAP–StaMPS open-source tools and GNSS data for PSI-Based ground deformation using dual-orbit sentinel-1 data: Accuracy assessment with error propagation analysis. Remote Sensing, 13(4): 753.
Orellana F, Delgado Blasco JM, Foumelis M, D’Aranno PJ, Marsella MA, Di Mascio P. 2020. Dinsar for road infrastructure monitoring: Case study highway network of Rome metropolitan (Italy). Remote Sensing, 12(22): 3697. doi:https://doi.org/10.3390/rs12223697.
Scheiber R, Moreira A. 2000. Coregistration of interferometric SAR images using spectral diversity. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2179-2191. doi:https://doi.org/10.1109/36.868876.
Takaku J, Tadono T, Tsutsui K, Ichikawa M. 2016. Validation of" AW3D" global DSM generated from Alos Prism. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3: 25. doi:http://dx.doi.org/10.5194/isprs-annals-III-4-25-2016.
Villasenor J, Zebker H. 1992. Temporal decorrelation in repeat pass-radar interferometry. In: In: IGARSS'92; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, May 26-29, 1992. Vol. 2 (A93-47551 20-43). Institute of Electrical and Electronics Engineers, Inc.
Zan De F, Guarnieri AM. 2006. TOPSAR: Terrain observation by progressive scans. IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2352-2360. doi:http://dx.doi.org/10.1109/TGRS.2006.873853.
Zarekamali M, Alhoseini Almodaresi SA, Naghdi K. 2017. Comparing the magnitude of the earth’s vertical relocation using the SBAS algorithm in X and C radar bands (Case study: Tehran lands). Journal of RS and GIS for Natural Resources, 8(3): 104-120. (In Persian).
Zhou X, Chang N-B, Li S. 2009. Applications of SAR interferometry in earth and environmental science research. Sensors, 9(3): 1876-1912. doi: https://doi.org/10.3390/s90301876.
Zhu XX, Wang Y, Montazeri S, Ge N. 2018. A review of ten-year advances of multi-baseline SAR interferometry using TerraSAR-X data. Remote Sensing, 10(9): 1374. doi:https://doi.org/10.3390/rs10091374.
