Land Subsidence Monitoring in Semarang, Indonesia through Optimized Hot Spot Analysis based on Time-Series InSAR Processing

Abstract

In Semarang, Indonesia, land subsidence had exacerbated the prone area of river floods due to heavy monsoon rain and coastal floods due to sea-level rise during high tide. Monitoring land subsidence in Semarang becomes essential to prevent the coastal inundation which leads the city to be submerged by seawater. In this study, land subsidence in Semarang was mapped using time-series analysis based on Stanford Methods for Persistent Scatterer (StaMPS) on the Sentinel-1 SAR datasets from March 2017 to May 2020 in both ascending and descending tracks. Optimized Hot Spot Analysis (OHSA) was conducted on the persistent scatterer points to spatially clustered the points with a high significance level statistically. The comparison of mean vertical deformation maps between two tracks shows a good correlation between displacement patterns. The land subsidence in Semarang was mainly due to groundwater extraction for industrial use and the compaction from the young alluvium soil.