Ground Stability Monitoring of Undermined and Landslide Prone Areas by Means of Sentinel-1 Multi-Temporal InSAR, Case Study from Slovakia

Abstract

Multi-temporal synthetic aperture radar interferometry techniques (MT-InSAR) are nowadays a well-developed remote sensing tool for ground stability monitoring of areas afflicted by natural hazards. Its application capability has recently been emphasized by the Sentinel-1 satellite mission, providing extensive spatial coverage, regular temporal sampling and free data availability. We perform MT-InSAR analysis over the wider Upper Nitra region in Slovakia, utilizing all Sentinel-1 images acquired since November 2014 until March 2017. This region is notable for its extensive landslide susceptibility as well as intensive brown coal mining. We focus on two case studies, being impaired by recent activation of these geohazards, which caused serious damage to local structures. We incorporate a processing chain based on open-source tools, combining the current Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS) implementation. MT-InSAR results reveal substantial activity at both case studies, exceeding the annual displacement velocities of 30 mm/year. Moreover, our observations are validated and their accuracy is confirmed via comparison with ground truth data from borehole inclinometers and terrestrial levelling. Detected displacement time series provide valuable insight into the spatio-temporal evolution of corresponding deformation phenomena and are thus complementary to conventional terrestrial monitoring techniques. At the same time, they not only demonstrate the feasibility of MT-InSAR for the assessment of remediation works, but also constitute the possibility of operational monitoring and routine landslide inventory updates, regarding the free Sentinel-1 data.

Observing Geohazards from Space

“…The extensive spatial coverage, regular temporal sampling, and free data availability of Sentinel-1 are further discussed in the paper by Czikhardt et al [10], who provide an in-depth multi-temporal study of recent ground stability and movements for a 440 km 2 study region in Slovakia characterised by high landslide susceptibility and intensive brown coal mining. The authors combine two open-source processing tools, i.e., the Sentinel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS), and compare their satellite-based observations with ground truth data from borehole inclinometers and terrestrial levelling to estimate the accuracy of the Sentinel-1 InSAR results.…”

A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China

“…Yang et al [30] applied a time series analysis and long short-term memory neural network to predict landslide displacement. The RS and permanent scatter interferometry synthetic aperture radar (PSInSAR) techniques are widely used to monitor the development of geohazards [31][32][33][34][35][36][37]. In addition, the most straightforward method to establish a territorial landslide early warning system is the definition of rainfall thresholds for landslide initiation [17,[38][39][40].…”

A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

“…Because Sentinel-1 applies the TOPS (Terrain Observation by Progressive Scan) imaging mode (De Zan and Guarnieri 2006), it is able to cover a wide area (Yagüe-Martínez et al 2016). This leads to images being captured within a series of overlapping regions (Czikhardt et al 2017). Therefore, the small difference within overlap bursts is advantageous for retrieving the horizontal motion of the ground parallel to the satellite path (Grandin et al 2016).…”