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Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring
In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506615/ https://www.ncbi.nlm.nih.gov/pubmed/32842663 http://dx.doi.org/10.3390/s20174751 |
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author | Karimzadeh, Sadra Matsuoka, Masashi |
author_facet | Karimzadeh, Sadra Matsuoka, Masashi |
author_sort | Karimzadeh, Sadra |
collection | PubMed |
description | In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km(2) of buildings and 65 km of pavement are at an immediate risk of land subsidence. |
format | Online Article Text |
id | pubmed-7506615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75066152020-09-26 Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring Karimzadeh, Sadra Matsuoka, Masashi Sensors (Basel) Article In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km(2) of buildings and 65 km of pavement are at an immediate risk of land subsidence. MDPI 2020-08-22 /pmc/articles/PMC7506615/ /pubmed/32842663 http://dx.doi.org/10.3390/s20174751 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Karimzadeh, Sadra Matsuoka, Masashi Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring |
title | Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring |
title_full | Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring |
title_fullStr | Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring |
title_full_unstemmed | Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring |
title_short | Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring |
title_sort | remote sensing x-band sar data for land subsidence and pavement monitoring |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7506615/ https://www.ncbi.nlm.nih.gov/pubmed/32842663 http://dx.doi.org/10.3390/s20174751 |
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