MAPPING SUSCEPTIBILITY TO GROUND DISPLACEMENT USING PS-INSAR AND PROBABILITY KRIGING TECHNIQUES: A CASE STUDY OF BUDUDA DISTRICT, UGANDA.

Ground displacement caused by both natural and anthropogenic factors poses a significant threat to human life and infrastructure. In this study, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was used to quantify the magnitude of ground displacement by analyzing multi-temporal Sentinel 1A and Sentinel 1B radar images. However, the PS-InSAR technique is affected by low coherence in vegetated areas and farm lands where there are no sufficient permanent targets from which a coherent radar signal is reflected. Consequently, this reduces the number of final PS points to be utilized for comprehensive ground displacement assessment. The study assesses the magnitude of ground displacement and maps the spatial variability of ground displacement probability in the Bududa district in eastern Uganda, an area susceptible to landslides. The PS-InSAR technique was employed to estimate Line of Sight (LOS) ground displacement from January 2017 to January 2025 while probability kriging, a geostatical analysis method, was utilized to predict ground displacement probability, including in areas where displacement was not detected by the PS-InSAR method. The PS-InSAR results showed that the Line of Sight (LOS) of ground displacement ranged from -80.16 mm (subsidence) to 40.08 mm (uplift) at rates of 5mm/yr and -10mm/yr respectively. The PS-InSAR LOS displacement results show stability in the north eastern part of Bududa near Mt. Elgon's rocky peak, and subsidence in central, southern, and western areas. The spatial variability of the ground displacement probability was modeled using spherical, exponential, and Gaussian semivariogram models at thresholds of 50.6 mm, 61 mm, and 71.7 mm; the results showed high spatial variability at lower thresholds and reduced variability at higher thresholds. The probability of displacement was low near the rocky Mt. Elgon peak and elevated elsewhere, underscoring the roles of underlying factors such as geology and soil type. This research demonstrates how geostatistics complements PS-InSAR to assess and continuously map ground displacement risk.