Urban ground deformation is an often overlooked but critical issue in rapidly developing coastal cities, particularly in arid regions where groundwater extraction and land modification are common. While time-series InSAR techniques are widely used in research, their integration into practical geospatial workflows for city-level applications remains limited.
This presentation demonstrates how time-series InSAR results can be effectively integrated into the QGIS environment to support ground deformation monitoring in Dammam, Saudi Arabia. Using Sentinel-1 data from 2017 to 2025, more than 1,500 interferograms were processed through an open-source workflow combining ASF HyP3, ISCE2, and MintPy. The analysis reveals localized subsidence with rates reaching up to −1.7 cm/year, forming a spatially coherent deformation pattern.
The focus of this work is on transforming these advanced remote sensing outputs into actionable geospatial information within QGIS. InSAR-derived products, including cumulative displacement maps, velocity layers, and time-series point data, are imported and managed in QGIS to enable visualization, spatial analysis, and integration with urban datasets. This allows users to identify deformation-prone zones and relate them to infrastructure, land use, and drainage systems.
A validation step using historical optical imagery shows that detected deformation corresponds to real surface changes, including infilled depressions and post-construction consolidation. This highlights the capability of QGIS to serve as a central platform for combining multi-source geospatial data into a coherent monitoring framework.
The presented workflow demonstrates how open-source tools can bridge the gap between advanced satellite analysis and practical urban management. It provides a scalable and cost-effective approach for municipalities and organizations seeking to incorporate ground deformation monitoring into their geospatial decision-making processes using QGIS.