Back to Search Start Over

Monitoring Subsidence in Urban Area by PSInSAR: A Case Study of Abbottabad City, Northern Pakistan.

Authors :
Khan, Rehan
Li, Huan
Afzal, Zeeshan
Basir, Muhammad
Arif, Muhammad
Hassan, Waqas
Cigna, Francesca
Lisi, Mariano
Source :
Remote Sensing; May2021, Vol. 13 Issue 9, p1651-1651, 1p
Publication Year :
2021

Abstract

Globally, major cities are experiencing fast settlement growth, which threatens the equilibrium of socio-ecosystems. In Pakistan, Abbottabad city in particular is experiencing fast urban growth. The main source of daily water usage for the population in these types of cities is groundwater (tube–wells). Excessive pumping and the high need for ground water for the local community are affecting the subsurface sustainability. In this study, the persistent scatterer interferometry synthetic aperture radar (PSInSAR) technique with synthetic aperture radar (SAR) images acquired from the Sentinel-1 were used to monitor ground subsidence in Abbottabad City, Northern Pakistan. To estimate the ground subsidence in Abbottabad City, SARPROZ software was employed to process a series of Sentinel-1 images, acquired from March 2017 to September 2019, along both descending and ascending orbit tracks. The subsidence observed in the results shows a significant increase from 2017 to 2019. The subsidence map shows that, during 2017, the subsidence was −30 mm/year and about −85 mm/year in 2018. While during 2019, the subsidence reached −150 mm/year. Thus, it has seen that, in the study area, the subsidence during these years increased with mean subsidence 60 mm/year. The overall trend of subsidence showed considerably high values in the center of the city, while areas away from the center of the city experienced low subsidence. Overall, the adopted methodology can be used successfully for detecting, mapping, and monitoring land surfaces vulnerable to subsidence. This will facilitate efficient planning, designing of surface infrastructure, and mitigation management of subsidence-induced hazards. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
13
Issue :
9
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
150372768
Full Text :
https://doi.org/10.3390/rs13091651