Creative Commons License

ABDİKAN S., Coskun S., Narin O., Bayik C., Calò F., Pepe A., ...More

39th International Symposium on Remote Sensing of Environment, ISRSE 2023, Antalya, Turkey, 24 - 28 April 2023, vol.48, pp.3-8 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 48
  • Doi Number: 10.5194/isprs-archives-xlviii-m-1-2023-3-2023
  • City: Antalya
  • Country: Turkey
  • Page Numbers: pp.3-8
  • Keywords: InSAR, Istanbul, landslide, LSTM, Sentinel-1, time-series
  • Hacettepe University Affiliated: Yes


This paper presents an initial analysis of predicting time series derived from long-term interferometric Synthetic Aperture Radar (InSAR) data. Time series analysis provides insights into the temporal evolution, variation, and dynamic nature of events. In this study, we focus on the Istanbul region, which is the most populous city in Turkey and spans both Europe and Asia. While the area is prone to seismic risks caused by active tectonic faults, it is also susceptible to other risks due to various phenomena. Therefore, this study investigates landslides triggered by geological structure and human-induced activities, particularly in Tepekent, a landslide-prone area in the town of Buyukcekmece located on the European side. We utilized the StaMPS persistent scatterer InSAR (PSI) method to detect slow movements over time. A total of 157 archive Copernicus Sentinel-1 data, acquired over the region between June 2017 and August 2022, were processed, primarily observing human structures with a maximum displacement amount of approximately 1 cm/year. About 500 persistent scatterer points were identified in the region, and the time series was formed by taking the average of these points. The Long Short-Term Memory (LSTM) neural network method was used to estimate motion. We trained the model with data from the first three years of the time series and used the data from the remaining two years for estimation, while the accuracy analysis was performed with the 5-year time series data. The RMSE values for the training and test data were determined to be 0.725 mm/year and 0.656 mm/year, respectively. Additionally, we estimated the time series for the period from August 2022 to August 2024. The observation and prediction results could be beneficial in developing efficient mitigation risk actions and sustainable urban management strategies.