Multi-Scale Ground Deformation Analysis and Investigation of Driver Factors Based on Remote Sensing Data: A Case Study of Zhuhai City

Abstract

Ground deformation poses an imminent threat to urban development. This study uses the multiscale geographically weighted regression (MGWR) model to investigate the spatial heterogeneity in factors influencing ground deformation, thereby elucidating the drivers behind regional variations in ground deformation patterns. To gain insights into the characteristics of ground deformation in Zhuhai, China, and its spatial relationship with natural and anthropogenic features, we initially utilized the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method to collect data on ground deformation and its distribution across the entire area. Concurrently, remote sensing imagery was used to identify the various mechanisms affecting ground deformation during the same period, including geotectonic conditions, geographic environment, and human activities. Subsequently, we used the MGWR model to quantitatively estimate the effects of these driving force factors on ground deformation in Zhuhai. Our findings reveal significant ground deformation in specific areas, including Baijiao Town (Doumen District), Hongqi Town (Jinwan District), the Gaolan Port Economic Zone, and the northern part of Hengqin Town, with peak deformation rates reaching 117 mm/y. Key drivers of ground deformation in Zhuhai include NDVI, groundwater extraction intensity, and soft soil thickness. The application of the MGWR model, with an R-sq value of 0.910, outperformed both the global regression model ordinary least squares (OLS), with an R-sq value of 0.722, and the local regression model geographically weighted regression (GWR), with an R-sq value of 0.770, in identifying driving forces. This study can provide valuable insights for government policies aimed at mitigating the disaster risks associated with urban ground deformation.