Multi-source Satellite Remote Sensing Techniques for Landslide Monitoring and Characterization

Landslides are natural geological hazards that pose significant threats, resulting in economic losses and casualties worldwide. Effective monitoring and characterization of landslides are crucial for understanding their evolution mechanisms and preventing catastrophic failures. While conventional field surveying methods provide accurate measurements of surface deformation, they are limited by high costs in terms of labor and time and uncertainties of arrangement for the ground-based equipment. The Satellite Interferometric Synthetic Aperture Radar (InSAR) technique has proven its application in landslide monitoring, offering advantages such as all-weather operations, wide spatial coverage, high spatial resolution, and high accuracy. InSAR can measure subtle changes along the SAR line-of-sight (LOS) direction but is not sensitive to movements along the north-south direction. Additionally, rapid movements during the failure stage can cause high decorrelation. On the other hand, satellite optical remote sensing data, combined with pixel offset tracking (POT) techniques, can measure large displacements in the horizontal plane. Moreover, multi-spectral analysis of optical images can offer insights into the spatial evolution of landslides. Therefore, the joint use of satellite InSAR and optical remote sensing techniques is complementary in landslide monitoring and characterization. However, the joint utilization of these techniques for capturing the long-term evolutions of landslides, particularly at their different stages using multi-source data, remains relatively unexplored. This dissertation aims to optimize and demonstrate the approaches for the joint use of satellite SAR and optical data in landslide monitoring and characterization across three distinct stages: pre-failure, failure, and post-failure. Three major landslides were studied in this dissertation. Firstly, the surface deformation of the 2017 Maoxian landslide during the pre-failure stage was captured using time series InSAR, while pre-failure slope features were detected from optical images. Secondly, the joint utilization of time series InSAR observations and optical analysis facilitated the monitoring of the pre-failure, failure, and post-failure stages of the 2020 Aniangzhai landslide. Lastly, the long-term post-failure deformation of the Huangtupo landslide in the Three Gorges Reservoir region was mapped using multi-source satellite SAR data, while the multi-temporal optical images were employed to investigate the long-term evolution of surface covers over the slope

Exploitation of large archives of ERS and ENVISAT C-band SAR data to characterize ground deformations

In the last few years, several advances have been made in the use of radar images to detect, map and monitor ground deformations. DInSAR (Differential Synthetic Aperture Radar Interferometry) and A-DInSAR/PSI (Advanced DInSAR/Persistent Scatterers Interferometry) technologies have been successfully applied in the study of deformation phenomena induced by, for example, active tectonics, volcanic activity, ground water exploitation, mining, and landslides, both at local and regional scales. In this paper, the existing European Space Agency (ESA) archives (acquired as part of the FP7-DORIS project), which were collected by the ERS-1/2 and ENVISAT satellites operating in the microwave C-band, were analyzed and exploited to understand the dynamics of landslide and subsidence phenomena. In particular, this paper presents the results obtained as part of the FP7-DORIS project to demonstrate that the full exploitation of very long deformation time series (more than 15 years) can play a key role in understanding the dynamics of natural and human-induced hazards. © 2013 by the authors

Standing on the shoulder of a giant landslide:A six-year long InSAR look at a slow-moving hillslope in the western Karakoram

In this work, we investigate a slow-moving, large landslide (∼20 km2) in the Chitral district in Northern Pakistan, near several villages. The slow-moving landslide was reported more than four decades ago but has never been examined afterward. Interferometric Synthetic Aperture Radar (InSAR) analyses, using Sentinel-1 data that span a period of six years, allowed us to retrieve the spatio-temporal pattern of hillslope deformation. We combined both ascending and descending orbits to identify vertical and horizontal deformations. Our results showed that the crown is moving relatively fast in comparison to the nearby regions; 30 mm/year and 40 mm/year in downward and eastward directions, respectively. Also, step-like deformations observed over the crown reflect a deep-seated landslide. At the footslope, on the other hand, we captured relatively high deformations but in an upward direction; specifically 30 mm/year and 30 mm/year in upward and eastward directions, respectively. We have discussed the possible roles of meteorologic and anthropogenic factors causing hillslope deformation occurred during the six-year period under consideration. We observed a seasonal deformation patterns that might be mainly interpreted to be governed by the influence of snowmelt due to increasing temperatures during the start of spring. Overall, the same mechanism might be present in many other hillslopes across the whole Hindukush-Himalayan-Karakoram range, where seasonal snowmelt is an active agent. In this context, this research provides a case study shedding a light on the hillslope deformation mechanism at the western edge of the Himalayan range.</p

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed

Analysing landslides in the Three Gorges Region (China) using frequently acquired SAR images

Spaceborne Synthetic Aperture Radar (SAR) sensors obtain regular and frequent radar images from which ground motion can be precisely detected using a variety of different techniques. The ability to remotely measure slope displacements over large regions has many uses and advantages, although the limitations of an increasingly common technique, Differential SAR Interferometry (D-InSAR), must be considered to avoid the misinterpretation of results. Areas of low coherence and the geometrical effects of mountainous terrain in SAR imagery are known to hinder the exploitation of D-InSAR results. A further major limitation for landslide studies is the assumption that variable rates of movement over a given distance cannot exceed a threshold value, dependent upon the SAR image pixel spacing, the radar sensor wavelength and satellite revisit frequency. This study evaluates the use of three SAR image modes from TerraSAR-X and ENVISAT satellites for monitoring slow-moving landslides in the densely vegetated Three Gorges region, China. Low coherence and episodically fast movements are shown to exceed the measureable limit for regular D-InSAR analysis even for the highest resolution, 11-day interferograms. Subsequently, sub-pixel offset time-series techniques applied to corner reflectors and natural targets are developed as a robust method of resolving time-variable displacements. Verifiable offsets are generated with the TerraSAR-X imagery and the precise movement history of landslides is obtained over a period of up to four years. The capability to derive two-dimensional movements from sub-pixel offsets is used to infer a rotational failure mechanism for the most active landslide detected, and a greater understanding of the landslide behaviour is achieved through comparisons with likely triggering factors and 2D limit equilibrium slope stability analysis

Investigating the Deformation of Slow Moving Landslides in the Northern Appennines of Italy with Differential Interferometry (InSAR)

The present work addresses different aspects of the analysis of landslide motion in the Northern Apennines of Italy by means of spaceborn radar interferometry (InSAR). Datasets from the satellite systems Envisat, COSMO SkyMed and Sentinel 1A were processed with different open source packages in order to assess technical issues related to different InSAR processing techniques and to obtain precise deformation measurements. One particular technical issue was the influence of the external digital elevation model, that is used to calculate and subtract the topographic phase, on final PS-InSAR and SBAS-results (chapter 3). It is common that different digital elevation models are available for a study area and often differences in final PS-InSAR/SBAS results can be observed. Due to the high landslide density in Northern Apennines, slope instabilities often interfere with man made structures. The case of Ripoli and Santa Maria Maddalena, South of Bologna, show in an interesting way this interaction between human activity and landslide deformation (chapter 4). Here, a double road tunnel was excavated under a slope that hosts several old deep seated landslides. Soon after excavation started in 2011 first deformations and damages on buildings were registered. InSAR derived deformation measurements document the landslide acceleration during the construction phase and show a decrease in displacement rates after the construction ceased. During the years 2013 and 2014 Northern Italy was struck by long enduring persistent rainfalls that caused numerous landslide reactivations. InSAR datasets were used to assess kinematics of 25 landslides during the years 2013 and 2015, hosted either by chaotic clay shales or pelitic turbidites (chapter 5). Deformation responses to precipitation were analysed in detail for eight selected cases, four of which are located in turbidites and four that are hosted by chaotic clay shales. Different deformation responses were discussed in relation to precipitation, morphology and landslide material