Document Type : Original Research Paper
Authors
1 Department of Geoinformtion and Geomatics Engineering, Facutly of Civil, Water, and Environment Engineering, Shahid Beheshti University, Tehran, Iran
2 Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
Abstract
Background and Objectives: Land subsidence is considered one of the most significant geomorphological hazards in arid and semi-arid regions, threatening groundwater resources, urban infrastructure, and agricultural lands for decades. In Iran, unregulated urban expansion and excessive groundwater extraction have intensified this phenomenon in major cities such as Tehran, Mashhad, Isfahan, and Shiraz. In particular, the eastern areas of Shiraz, characterized by alluvial soils, high building density, and sharp declines in groundwater levels, have become one of the primary hotspots of subsidence in southern Iran. Given the high potential of Sentinel-1 radar data for analyzing land deformation and the effectiveness of DInSAR in rapid monitoring, this study aims to analyze the spatiotemporal patterns of land subsidence in eastern Shiraz, investigate natural and anthropogenic contributing factors, and propose solutions for risk mitigation and support of sustainable urban development.
Methods: This study employed 24 Sentinel-1A SAR images (IW mode, VV polarization) from 2015 to 2025. Processing was conducted in SNAP software. Orbital corrections were applied using POD files, followed by radiometric calibration to extract Sigma0 values. A 7×7 Lee filter was used to reduce speckle noise. Fifteen image pairs with temporal baselines less than 365 days and perpendicular baselines below 150 meters were selected to generate interferograms. Phase unwrapping was performed using the SNAPHU algorithm with the Minimum Cost Flow (MCF) method. To minimize atmospheric effects, image pairs with similar humidity were chosen, and additional filtering included the Goldstein filter, topographic masking, and variogram analysis. The final phase data were analyzed statistically using mean, skewness, and kurtosis, as well as spatially through Moran’s I. Multiple regression analysis was also conducted to evaluate the influence of groundwater extraction, soil type, building density, and slope on the observed subsidence rates.
Findings: The results showed an average subsidence rate of 18.4 mm/year with a standard deviation of 8.2 mm. Three main subsidence hotspots with rates of 25–45 mm/year were identified in the north, center, and south of the study area. Statistical analysis indicated a positively skewed distribution (1.23) with a kurtosis of 2.87. Multivariate regression analysis showed that groundwater extraction (β = 0.78, p < 0.001) was the most influential factor. Soil type (clay), building density, and slope also had significant effects, with positive and negative contributions. Moran’s, I test confirmed a clustered spatial pattern of subsidence (I = 0.742).
Conclusion: DInSAR proved to be an effective and relatively accurate tool for monitoring land subsidence, especially in regions with limited in-situ data. This study underscores the significant role of human activities in exacerbating land subsidence and highlights the need for continuous monitoring, smart supervisory systems, and a reassessment of urban development patterns. Suggested future directions include developing machine learning models with Sentinel-1 data, integrating GNSS observations to enhance accuracy, and conducting land use change analysis using Landsat and Sentinel-2 imagery. The main limitations of the study were the lack of up-to-date groundwater level data and the temporal sparsity of some satellite images.
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© 2025 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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