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Article

Assessment of Urban Subsidence in the Lisbon Metropolitan Area (Central-West of Portugal) Applying Sentinel-1 SAR Dataset and Active Deformation Areas Procedure

by
José Cuervas-Mons
1,*,
José Luis Zêzere
2,
María José Domínguez-Cuesta
1,
Anna Barra
3,
Cristina Reyes-Carmona
4,
Oriol Monserrat
3,
Sergio Cruz Oliveira
2 and
Raquel Melo
2,5
1
Department of Geology, University of Oviedo, 33005 Oviedo, Spain
2
Institute of Geography and Spatial Planning, University of Lisbon, 1600-276 Lisbon, Portugal
3
Geomatics Division, Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), 08860 Castelldefels, Spain
4
Department of Geodynamics, University of Granada, 18071 Granada, Spain
5
Department of Geosciences, University of Evora, 7000-671 Evora, Portugal
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(16), 4084; https://doi.org/10.3390/rs14164084
Submission received: 21 July 2022 / Revised: 9 August 2022 / Accepted: 16 August 2022 / Published: 20 August 2022
(This article belongs to the Special Issue Monitoring Geohazard from Synthetic Aperture Radar Interferometry)

Abstract

The Lisbon metropolitan area (LMA, central-west of Portugal) has been severely affected by different geohazards (flooding episodes, landslides, subsidence, and earthquakes) that have generated considerable damage to properties and infrastructures, in the order of millions of euros per year. This study is focused on the analysis of subsidence, as related to urban and industrial activity. Utilizing the A-DInSAR dataset and applying active deformation areas (ADA) processing at the regional scale has allowed us to perform a detailed analysis of subsidence phenomena in the LMA. The dataset consisted of 48 ascending and 61 descending SAR IW-SLC images acquired by the Sentinel-1 A satellite between January 2018 and April 2020. The line-of-sight (LOS), mean deformation velocity (VLOS) maps (mm year−1), and deformation time series (mm) were obtained via the Geohazard Exploitation Platform service of the European Space Agency. The maximum VLOS detected, with ascending and descending datasets, were −38.0 and −32.2 mm year−1, respectively. ADA processing over the LMA allowed for 592 ascending and 560 descending ADAs to be extracted and delimited. From the VLOS measured in both trajectories, a vertical velocity with a maximum value of −32.4 mm year−1 was estimated. The analyzed subsidence was associated to four ascending and three descending ADAs and characterized by maximum VLOS of −25.5 and −25.2 mm year−1. The maximum vertical velocity associated with urban subsidence was −32.4 mm year−1. This subsidence is mainly linked to the compaction of the alluvial and anthropic deposits in the areas where urban and industrial sectors are located. The results of this work have allowed to: (1) detect and assess, from a quantitative point of view, the subsidence phenomena in populated and industrial areas of LMA; (2) establish the relationships between the subsidence phenomena and geological and hydrological characteristics.
Keywords: A-DInSAR; ADA; Sentinel-1; urban subsidence; Lisbon metropolitan area A-DInSAR; ADA; Sentinel-1; urban subsidence; Lisbon metropolitan area
Graphical Abstract

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MDPI and ACS Style

Cuervas-Mons, J.; Zêzere, J.L.; Domínguez-Cuesta, M.J.; Barra, A.; Reyes-Carmona, C.; Monserrat, O.; Oliveira, S.C.; Melo, R. Assessment of Urban Subsidence in the Lisbon Metropolitan Area (Central-West of Portugal) Applying Sentinel-1 SAR Dataset and Active Deformation Areas Procedure. Remote Sens. 2022, 14, 4084. https://doi.org/10.3390/rs14164084

AMA Style

Cuervas-Mons J, Zêzere JL, Domínguez-Cuesta MJ, Barra A, Reyes-Carmona C, Monserrat O, Oliveira SC, Melo R. Assessment of Urban Subsidence in the Lisbon Metropolitan Area (Central-West of Portugal) Applying Sentinel-1 SAR Dataset and Active Deformation Areas Procedure. Remote Sensing. 2022; 14(16):4084. https://doi.org/10.3390/rs14164084

Chicago/Turabian Style

Cuervas-Mons, José, José Luis Zêzere, María José Domínguez-Cuesta, Anna Barra, Cristina Reyes-Carmona, Oriol Monserrat, Sergio Cruz Oliveira, and Raquel Melo. 2022. "Assessment of Urban Subsidence in the Lisbon Metropolitan Area (Central-West of Portugal) Applying Sentinel-1 SAR Dataset and Active Deformation Areas Procedure" Remote Sensing 14, no. 16: 4084. https://doi.org/10.3390/rs14164084

APA Style

Cuervas-Mons, J., Zêzere, J. L., Domínguez-Cuesta, M. J., Barra, A., Reyes-Carmona, C., Monserrat, O., Oliveira, S. C., & Melo, R. (2022). Assessment of Urban Subsidence in the Lisbon Metropolitan Area (Central-West of Portugal) Applying Sentinel-1 SAR Dataset and Active Deformation Areas Procedure. Remote Sensing, 14(16), 4084. https://doi.org/10.3390/rs14164084

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