Integrating SBAS-InSAR Deformation Monitoring and AHP-GIS for Landslide Susceptibility Assessment Along the Ghazi-i-Millat Corridor, Azad Jammu and Kashmir, Pakistan
Basit Ali Khan
Post Doctoral Researcher in the school of “Surveying and Land Information Engineering, Henan Polytechnic University”, Henan, China
Saad Ali Khan
SAADKHAN&CO Pvt. Ltd. Pakistan
Muhammad Toseef Adnan
UCAS, China
Usman Basharat
Jilin University, China
Khawaja Izhar Ahmed
Shenzhen University, China
Bright Sampana Adoko
Chang’an University, Xi’an China
Han Wu
Chang’an University, Xi’an China
Asim Javed
Ulster University Magee Campus, Londonderry, Northern Ireland, United Kingdom.
Raja Saad Qamar
University of Bremen, Germany
DOI: https://doi.org/10.59429/ersr.v4i1.14820
Keywords: Small Baseline Synthetic Aperture Radar (SBAS), Analytic Hierarchy Process (AHP), Ghazi-Millat Road (GMR), Landslides
Abstract
This study applies the Small Baseline Subset InSAR (SBAS-InSAR) technique, together with an Analytic Hierarchy Process (AHP)-GIS weighted-overlay framework, to assess surface deformation and landslide susceptibility along the Ghazi-i-Millat Road (GMR), Azad Jammu and Kashmir, Pakistan. Time-series analysis of 91 Sentinel-1 acquisitions (January 2021-December 2023) indicates a mean Line-of-Sight (LOS) velocity ranging from -14.50 to +15.00 mm/year, with a maximum cumulative LOS displacement of approximately +130 mm and -96 mm over the three-year observation period; a localized maximum LOS velocity of 17.07 mm/year was also recorded. Because LOS displacement records motion toward or away from the sensor rather than true vertical or slope-parallel movement, these values are interpreted as indicators of possible ground instability rather than direct proof of active landsliding. Landslide susceptibility mapping incorporates eight causative parameters, slope gradient, slope aspect, geology, drainage network, fault proximity, road proximity, NDVI, and NDWI, combined through AHP-derived weights in a GIS weighted-overlay model. Dense vegetation cover is associated with qualitatively reduced SBAS-InSAR coherence and data reliability, consistent with well-documented vegetation-induced radar decorrelation. The weighted-overlay analysis assigns the greatest relative importance to slope gradient, followed by drainage network and road proximity, and the resulting susceptibility map places the majority of the mapped landslide inventory within the considerable-to-very-high susceptibility classes. The findings support the value of combining SBAS-InSAR deformation monitoring with AHP-GIS susceptibility mapping as a regional decision-support tool for infrastructure planning, slope stabilization, and drainage management along this economically important corridor, while underscoring the need for independent field validation, ascending/descending geometric decomposition, and quantitative uncertainty assessment in future work.
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