Evaluation of the usability of sentinel-1 data in InSAR applications for detecting surface deformation

Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the landslide-prone region of Buyukalan, Antalya. Multi-temporal InSAR analysis of Sentinel-1 data (between 2015–2020) is performed using LiCSAR-LiCSBAS, followed by two novel approaches: Multi-dimensional InSAR Research and Analysis (MIRA) and Crosta's InSAR application (InCROSS). Cumulative LOS velocity maps reveal deformation rates of −10,996 mm/year to 10,449 mm/year for descending tracks and -38,496 mm/year to 37,957 mm/year for ascending tracks. Vertical displacements range from -19,401 mm/year to 23,173 mm/year and east–west components from -28,515 mm/year to 29,104 mm/year. MIRA uses an n-dimensional Visualizer and SVM classifier to identify deformation clusters and InCROSS applies PCA to enhance deformation features. MIRA increases deformation detection capacity compared to conventional InSAR products and InCROSS integrates these products. A comparison of the results reveals 80,48% consistency between them. Overall, the integration of InSAR with statistical and multidimensional analysis significantly enhances the detection and interpretation of ground deformation patterns in landslide prone areas.