Slow-moving landslides are natural phenomena that shape mountainous landscapes over long time scales. These landslides creep at rates ranging from meters to millimeters per year, often unnoticed by people who build houses on them, unaware of the risks. Although slow-moving landslides rarely cause fatalities, they can be triggered into fast-moving landslides by earthquakes, leading to catastrophic outcomes. Therefore, understanding and monitoring these landslides is crucial to mitigate potential hazards, particularly in seismically active regions.

Traditional methods for identifying and monitoring slow-moving landslides rely on fieldwork and in-situ instruments. While these methods are accurate, they are limited in spatial and temporal coverage. Recent advancements in remote sensing, particularly Interferometric Synthetic Aperture Radar (InSAR), offer extensive, high-resolution monitoring capabilities, enabling detailed observations of ground movements. InSAR studies have used Line Of Sight (LOS) velocities to monitor the be- havior of slow-moving landslides. However, these findings do not provide details on the direction of movement, as LOS velocities are always measured along a single dimension. This limitation makes it impossible to determine the direction and type of movement, which are essential for accurate risk as- sessment and developing early warning systems by identifying deformation patterns that may indicate imminent failure.

This thesis aims to investigate the temporal evolution of a slow-moving landslide subjected to an earthquake, focusing on changes in rate and direction, to identify the type of movements. To achieve this, the Multi-Temporal InSAR (MT-InSAR) technique is used, resulting in Persistent Scatterers (PSs) as measuring points to observe changes in the rate and direction of landslide movements post- earthquake. Sentinel-1 data from both ascending and descending orbital configurations were used to obtain LOS velocity time series at the PSs both before and after the earthquake. By combining LOS velocities from both PS orbital configurations, the method decomposed LOS velocities into vertical and horizontal components, providing both changes in velocities and direction, which together enabled the identification of the type of movement.

To test this method, a slow-moving landslide located in Büyükçekmece, Istanbul, was chosen as a case study. This landslide was impacted by a Mw 5.7 earthquake on 26 September 2019. Within the landslide, an urbanized area was selected to analyze both the uniform and spatially variable re- sponses. The uniform results involved averaging the velocities at the PSs, while the individual PSs were analyzed to identify spatially variable movement patterns.

The uniform results indicated three main phases in landslide behavior after the earthquake: an initial translational movement following the earthquake, a roto-translational phase combining rotation and translation, and a new constant state characterized by horizontal spreading. At an individual point level, the results showed that most points transitioned from moving vertically downward to upward, indicating a significant change in underlying mechanisms, highlighting the landslide’s complexity.

The monitored behavior resembles that of an inverse creeping landslide, where after the initial disturbance, the landslide decelerates to a new constant state. Based on the collected real data, it is possible to construct hypothetical risk scenarios with behavior curves characterized by continuous acceleration, which would pose an immediate threat of catastrophic failure. Although this earthquake did not cause the Büyükçekmece landslide to fail, and the landslide decelerated to a constant state, it is crucial to continue monitoring as there are signs of instability and conditions may change.