Geohazard analysis using field and satellite remote sensing data

Abstract

At a time when climate change is increasing the frequency and severity of extreme weather events, disasters will continue to be significant impediments to sustainable development worldwide. Therefore, investing in disaster risk reduction is a precondition for developing a sustainable environment, intelligent urban systems, resilient infrastructure, and energy infra-system in a changing climate. This doctoral dissertation centers on natural and anthropogenic disasters, providing a rigorous understanding of where and when ground instabilities mobilize and supplies invaluable information on triggers and the spatiotemporal extent of an impending disaster. The hydro-geomechanical responses of wildfire-affected soils are assessed by performing a series of index tests, saturated direct shear strength tests, and saturated hydraulic conductivity tests on field soil samples. The results show that the continuous deterioration of roots highly depends on the soil burn severity. The root’s deterioration reduces the soil’s cohesion intercept, with the friction angle unaffected. One month and four months post-wildfire, the hydraulic conductivity of burned soils is lower than the unburned soil. However, six months post-wildfire, the hydraulic conductivity of burned soils increases by approximately twice that of unburned soils. The increased hydraulic conductivity is due to macropore flow paths formed by impoverished roots. The shear strength reduction and increased hydraulic conductivity of burned soils led to exploring free-to-use data and monitoring tools for the potentially unstable burned slopes. Pre- and post-landslide analyses are performed using Sentinel-1 Synthetic Aperture Radar (S1 SAR) data. Specifically, the Persistent Scatterer Interferometric SAR (PS-InSAR) and Small Baseline Subset (SBAS) techniques are applied to monitor precursory slope movements. The monitoring effort extends to applying the inverse velocity (IV) method for time-of-failure (TOF) prediction. The measure of S1 SAR amplitude change is used to delineate the landslide extent. Results show that the PS-InSAR and SBAS techniques map the accelerating ground surfaces of the investigated slopes. Accordingly, the PS-InSAR technique tracks the temporal evolution of the progressive accelerations of the monitored sites before the landslide. However, the SBAS technique underestimates the areas’ line-of-sight (LOS) velocity compared to the PS-InSAR results. The IV method predicts the TOF, with ±1–4 days precision, where accelerating trends occur. The measure of S1 SAR amplitude changes also reasonably delineates the post-landslide area. Likewise, the PS-InSAR technique based on S1 SAR data investigates uneven subsidence due to tunneling. The PS-InSAR results are compared to leveling data. The LOS velocity map of the study site reveals a localized subsidence trough associated with the tunnel construction. The LOS displacement time series derived from the PS-InSAR analysis and modeled by Gompertz’s function successfully track the spatiotemporal progression of subsidence related to the phases of tunnel construction. The maximum subsidence rate in the radar LOS direction exceeds 40 mm/yr, with maximum subsidence of 200 mm. The results clearly show that dewatering during tunneling aggravated the subsidence phenomenon. The consequent compaction and consolidation of the compressible soil layers due to the lowered groundwater table are the leading causes of subsidence. Overall, the synergistic use of field and satellite data, together with other layers of spatiotemporal information, offers the potential implementation of an operational monitoring system for the early detection of ground instabilities, leading to robust geotechnical modeling, timely forecasting, and adequate early warning.