MONITORING OF LAND SURFACE DISPLACEMENT BASED ON SBAS-INSAR TIME-SERIES AND GIS TECHNIQUES: A CASE STUDY OVER THE SHIRAZ METROPOLIS, IRAN
Keywords: Remote Sensing, Synthetic Aperture Radar, Deformation, Small Baseline Subset, Fuzzy Membership
Abstract. Nowadays, subsidence as one of the natural disasters, threaten urban environments. Over the past three decades, the small baseline subset (SBAS) InSAR method due to its capability to monitor medium to large-scale deformations, has emerged as an important tool for estimating surface displacements. In densely vegetated environments, however, the SBAS technique is not effective due to the decorrelation effect. Since the SBAS technique can estimate millions of points for subsidence, these data can be used as input to the Kriging method to estimate the unknown points. Therefore, in this study, we identified the subsidence disaster-stricken-urban areas in the Shiraz metropolis over a 4-year period (between 2017 and 2021) based on the fusion of the SBAS technique and geospatial information science (GIS) using Sentinel-1A time-series images. In the GIS part of this research, the ordinary kriging method was used for interpolation of the SBAS data. Also, the kernel estimation density (KED) was used for the preparation of the raster layers of a population and the built-up density. Based on 33 radar images in ascending and descending orbit, a total of 95 interferograms have been computed. In this research, temporal baselines of less than 100 meters and multi-look window dimensions of 200 meters were used. What is more, the amount of coherence was 0.8 in estimating displacements. Based on the results, the highest displacements occurred in districts 2, 4, 7, and 11. The maximum value of the displacement (30 mm per year) was seen in the southeast of the Shiraz metropolis, in district 7. Moreover, the surface displacement was mainly observed in the lands of the Shiraz international airport and some of the agricultural lands located in district 2. The reasons for the displacements in districts 4 and 11 are related to the high density of the building coverages in these regions. Also, the increased population density was another reason for the subsidence in district 4. It can be concluded that the suggested framework is efficient in determining time-series surface movements in extensive urban and non-urban areas.