Assessing subsidence of Mexico City from InSAR and LandSat ETM+ with CGPS and SVM

Abstract

Abstract This study presents an enhanced analysis of the subsidence rates and their effects on Mexico City. As a result of excess water withdrawal, Mexico City is experiencing subsidence. We integrated and analyzed Interferometric Synthetic Aperture Radar (InSAR), Continuous Global Positioning Systems (CGPS), and optical remote sensing data to analyze Mexico City’s subsidence. This study utilized 52 ENVISAT-ASAR, nine GPS stations, and one Landsat ETM+ image from the Mexico City area to understand better the subsidence rates and their effects on Mexico City’s community. The finding of this study reveals a high amount of correlation (up to 0.98) between two independent geodetic methods. We also implemented the Support Vector Machine (SVM) analysis method based on Landsat ETM+ image to classify Mexico City’s population density. We used SVM to compare Persistent Scatterer Interferometry (PSI) subsidence rates with the buildings’ distribution densities. This integrated study shows that the fastest subsidence zone (i.e., areas greater than 100 mm/yr), which falls into the above-mentioned temporal baseline, occurs in high and moderate building distribution density areas.

Keywords

• Mexico City
• Subsidence
• InSAR
• GPS
• PSI
• SVM