The challenge to use multi-temporal InSAR for landslide early warning

Schlögl, Matthias; Gutjahr, Karlheinz; Fuchs, Sven

doi:10.1007/s11069-022-05289-9

Cited by 14 publications

(2 citation statements)

References 19 publications

(19 reference statements)

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“…-Geomorphological mapping, topographic maps, and aerial photography (Parise 2001) -Spaceborne, airborne, and terrestrial remote sensing technologies (Guzzetti et al 2012) -Synthetic Aperture Radar (SAR; Modini et al 2021) and multitemporal interferometric SAR (Schlögl et al 2022) Our work complements and updates these reviews with a unique focus on satellite data and how better observing methodologies and additional EO systems have improved our capabilities in landslide mapping. Compared to such recent reviews, our work provides the additional focus on how recent technologies are drastically changing the way EO is deployed in landslide mapping and based on the evolution of such discipline, the manuscript provides areas where future research can focus to tackle existing challenges.…”

Section: Introductionmentioning

confidence: 94%

Mapping landslides from space: A review

Novellino,

Pennington,

Leeming

et al. 2024

Landslides

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Landslide hazards have significant social, economic, and environmental impact. This work provides a critical review of the main existing literature using satellite data for mapping landslides. We created and examined an extensive bibliographic database from Web of Science (WoS) consisting in 291 outputs from > 1,000 authors who studied almost 700,000 landslides across all continents, for a total of 52 countries represented with China and Italy on top of the list with more authors. The outputs are equivalent to ~ 5% of the whole landslide-related production for the period 1996–2022, with a 600% increase in the number of papers after 2014 driven by the availability of Sentinel-1 and Sentinel-2 data. Analysis of the geographical location across the 66 different countries analysed shows that, within the total number of contributions, the satellite imagery was used to detect and map two main types of landslides: flows and slides. When specified in the manuscripts, the events have been triggered by rainfall (104 cases), earthquakes (32 cases), or both (17 cases). Slope instabilities in these areas were predominantly identified through manual detection (40%); but since 2020, the advent of artificial intelligence is suppressing all other techniques. Despite the undisputed progress of EO-based landslide mapping over the last 26 years, which makes it a consolidated tool for many landslide-related applications, challenges still remain for an effective and operational use of EO images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions.

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Section: Introductionmentioning

confidence: 94%

Mapping landslides from space: A review

Novellino,

Pennington,

Leeming

et al. 2024

Landslides

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“…Our study expands on a recent application of this approach [29] in two ways: (1) combining both ascending and descending satellite observation geometries to retrieve the vertical and east-west displacement time series to get a more accurate estimation of the vertical movement, and (2) introducing a change detection method to determine when and in which manner the deformation trend changes over time to aid in further interpretation of the data. It is often the case that a single linear model cannot adequately describe the evolution of displacement over time; instead, multiple linear/polynomial relationships applying to different time spans may be more appropriate [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

A Clustering Approach for the Analysis of InSAR Time Series: Application to the Bandung Basin (Indonesia)

Rygus,

Novellino,

Hussain

et al. 2023

Remote Sensing

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Interferometric Synthetic Aperture (InSAR) time series measurements are widely used to monitor a variety of processes including subsidence, landslides, and volcanic activity. However, interpreting large InSAR datasets can be difficult due to the volume of data generated, requiring sophisticated signal-processing techniques to extract meaningful information. We propose a novel framework for interpreting the large number of ground displacement measurements derived from InSAR time series techniques using a three-step process: (1) dimensionality reduction of the displacement time series from an InSAR data stack; (2) clustering of the reduced dataset; and (3) detecting and quantifying accelerations and decelerations of deforming areas using a change detection method. The displacement rates, spatial variation, and the spatio-temporal nature of displacement accelerations and decelerations are used to investigate the physical behaviour of the deforming ground by linking the timing and location of changes in displacement rates to potential causal and triggering factors. We tested the method over the Bandung Basin in Indonesia using Sentinel-1 data processed with the small baseline subset InSAR time series technique. The results showed widespread subsidence in the central basin with rates up to 18.7 cm/yr. We identified 12 main clusters of subsidence, of which three covering a total area of 22 km2 show accelerating subsidence, four clusters over 52 km2 show a linear trend, and five show decelerating subsidence over an area of 22 km2. This approach provides an objective way to monitor and interpret ground movements, and is a valuable tool for understanding the physical behaviour of large deforming areas.

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