Landslides have been one of the major natural disasters in most countries in the world. Indonesia has experienced landslides events annually in its mountainous areas and has been generating increasing number of casualties in recent years. One way of preventing the number of casualties from these landslides is by identifying those landslides prone areas and mitigates it by providing susceptible maps. Remote Sensing (RS) and Geographic Information Systems (GIS) are 2 growing modern technologies that have been used by many researchers in the provision of Landslides Susceptibility Maps (LSM). LSM has been created by many kinds approach by simple methodology like overlaying several parameters layers in GIS to combined methodologies such as using statistical approach and validation with remote sensing images or ground survey. The study area covers a mountainous area named Bawakaraeng and Lompobattang Mountain in South Sulawesi Province, Indonesia where rock formations are dominated by Miocene erupted volcanic. The objective of this research is trying to enhance the existing LSM created using frequency ratio model with higher resolution raster image of causal factors parameters used to create the LS index maps. In this research, we attempted to use the raster image created from Differential Interferometry of Synthetic Aperture Radar (SAR) image processing of ALOS PALSAR1 images of DInSAR repeated-pass method. The raw data is SAR level 1 data with 5 scenes of different acquisition year of 2007, 2008, 2009, 2010, and 2011 of similar seasons. We have processed 3 pairs of SAR and the raster image generated have indicated areas where slight surface displacement have occurred and confirmed where cracks were found that initiated surface movement of future landslides. This image was used to validate the landslide incidence location and as one parameter of the causal factors in frequency ratio analysis in enhancing the creation of LSM. The result showed zone of prone areas to landslides graded based on the Landslide Susceptibility Index.