Landslide Characteristics and Evolution: What We Can Learn from Three Adjacent Landslides

Landslide processes are a consequence of the interactions between their triggers and the surrounding environment. Understanding the differences in landslide movement processes and characteristics can provide new insights for landslide prevention and mitigation. Three adjacent landslides characterized by different movement processes were triggered from August to September in 2018 in Hualong County, China. A combination of surface and subsurface characteristics illustrated that Xiongwa (XW) landslides 1 and 2 have deformed several times and exhibit significant heterogeneity, whereas the Xiashitang (XST) landslide is a typical retrogressive landslide, and its material has moved downslope along a shear surface. Time-series Interferometric Synthetic Aperture Radar (InSAR) and Differential InSAR (DInSAR) techniques were used to detect the displacement processes of these three landslides. The pre-failure displacement signals of a slow-moving landslide (the XST landslide) can be clearly revealed by using time-series InSAR. However, these sudden landslides, which are a typical catastrophic natural hazard across the globe, are easily ignored by time-series InSAR. We confirmed that effective antecedent precipitation played an important role in the three landslides’ occurrence. The deformation of an existing landslide itself can also trigger new adjacent landslides in this study. These findings indicate that landslide early warnings are still a challenge since landslide processes and mechanisms are complicated. We need to learn to live with natural disasters, and more relevant detection and field investigations should be conducted for landslide risk mitigation.

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InSAR Monitoring of Landslide Activity in Dominica

Remote Sensing ◽

10.3390/rs13040815 ◽

2021 ◽

Vol 13 (4) ◽

pp. 815

Author(s):

Mary-Anne Fobert ◽

Vern Singhroy ◽

John G. Spray

Keyword(s):

Risk Mitigation ◽

Disaster Mitigation ◽

Aerial Images ◽

Landslide Inventory ◽

Landslide Risk ◽

Susceptibility Map ◽

Interferometric Synthetic Aperture Radar ◽

Rainfall Events ◽

Susceptibility Maps ◽

Digital Elevation

Dominica is a geologically young, volcanic island in the eastern Caribbean. Due to its rugged terrain, substantial rainfall, and distinct soil characteristics, it is highly vulnerable to landslides. The dominant triggers of these landslides are hurricanes, tropical storms, and heavy prolonged rainfall events. These events frequently lead to loss of life and the need for a growing portion of the island’s annual budget to cover the considerable cost of reconstruction and recovery. For disaster risk mitigation and landslide risk assessment, landslide inventory and susceptibility maps are essential. Landslide inventory maps record existing landslides and include details on their type, location, spatial extent, and time of occurrence. These data are integrated (when possible) with the landslide trigger and pre-failure slope conditions to generate or validate a susceptibility map. The susceptibility map is used to identify the level of potential landslide risk (low, moderate, or high). In Dominica, these maps are produced using optical satellite and aerial images, digital elevation models, and historic landslide inventory data. This study illustrates the benefits of using satellite Interferometric Synthetic Aperture Radar (InSAR) to refine these maps. Our study shows that when using continuous high-resolution InSAR data, active slopes can be identified and monitored. This information can be used to highlight areas most at risk (for use in validating and updating the susceptibility map), and can constrain the time of occurrence of when the landslide was initiated (for use in landslide inventory mapping). Our study shows that InSAR can be used to assist in the investigation of pre-failure slope conditions. For instance, our initial findings suggest there is more land motion prior to failure on clay soils with gentler slopes than on those with steeper slopes. A greater understanding of pre-failure slope conditions will support the generation of a more dependable susceptibility map. Our study also discusses the integration of InSAR deformation-rate maps and time-series analysis with rainfall data in support of the development of rainfall thresholds for different terrains. The information provided by InSAR can enhance inventory and susceptibility mapping, which will better assist with the island’s current disaster mitigation and resiliency efforts.

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A multi-scale methodological approach for slow-moving landslide risk mitigation in urban areas, southern Italy

Euro-Mediterranean Journal for Environmental Integration ◽

10.1007/s41207-019-0110-4 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 8

Author(s):

Settimio Ferlisi ◽

Giovanni Gullà ◽

Gianfranco Nicodemo ◽

Dario Peduto

Keyword(s):

Urban Areas ◽

Southern Italy ◽

Risk Mitigation ◽

Methodological Approach ◽

Landslide Risk ◽

Multi Scale ◽

Slow Moving

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Small-scale analysis to rank municipalities requiring slow-moving landslide risk mitigation measures: the case study of the Calabria region (southern Italy)

Geoenvironmental Disasters ◽

10.1186/s40677-021-00202-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Giovanni Gullà ◽

Gianfranco Nicodemo ◽

Settimio Ferlisi ◽

Luigi Borrelli ◽

Dario Peduto

Keyword(s):

Southern Italy ◽

Risk Mitigation ◽

Mitigation Measures ◽

Economic Losses ◽

Phase Method ◽

Small Scale ◽

Landslide Risk ◽

Calabria Region ◽

Slow Moving ◽

Gis Environment

AbstractThis paper proposes a three-phase method that combines multi-source (i.e. topographic, thematic, monitoring) input data in a GIS environment to rank—at small (1:250,000) scale—administrative units (e.g. municipalities) based on their exposure to slow-moving landslide risk within a selected area (e.g. a region) and, accordingly, detect those primarily requiring mitigation measures. The method is applied in the Calabria region (southern Italy) where several municipalities are widely affected by slow-moving landslides that systematically cause damage to buildings and infrastructure networks resulting in significant economic losses. The results obtained are validated based on the information gathered from previous studies carried out at large (municipal) scale. The work undertaken represents a first, fundamental step of a wider circular approach that can profitably facilitate the decision makers in addressing the issue of the slow-moving landslide risk mitigation in a sustainable way.

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Full integration of geomorphological, geotechnical, A-DInSAR and damage data for detailed geometric-kinematic features of a slow-moving landslide in urban area

Landslides ◽

10.1007/s10346-020-01541-0 ◽

2020 ◽

Author(s):

Dario Peduto ◽

Mariantonia Santoro ◽

Luigi Aceto ◽

Luigi Borrelli ◽

Giovanni Gullà

Keyword(s):

Urban Areas ◽

Structural Model ◽

Risk Mitigation ◽

Integrated Approach ◽

Mitigation Strategies ◽

Landslide Risk ◽

Conditioning Factors ◽

Source Data ◽

Slow Moving ◽

Set Up

Abstract The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period.

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Accuracy of a Model-Free Algorithm for Temporal InSAR Tropospheric Correction

Remote Sensing ◽

10.3390/rs13030409 ◽

2021 ◽

Vol 13 (3) ◽

pp. 409

Author(s):

Howard Zebker

Keyword(s):

Time Series ◽

Signal To Noise Ratio ◽

Artifact Reduction ◽

Interferometric Synthetic Aperture Radar ◽

Spatial Properties ◽

Model Free ◽

Weather Model ◽

Phase Variations ◽

Source Of Error ◽

Signal Correlation

Atmospheric propagational phase variations are the dominant source of error for InSAR (interferometric synthetic aperture radar) time series analysis, generally exceeding uncertainties from poor signal to noise ratio or signal correlation. The spatial properties of these errors have been well studied, but, to date, their temporal dependence and correction have received much less attention. Here, we present an evaluation of the magnitude of tropospheric artifacts in derived time series after compensation using an algorithm that requires only the InSAR data. The level of artifact reduction equals or exceeds that from many weather model-based methods, while avoiding the need to globally access fine-scale atmosphere parameters at all times. Our method consists of identifying all points in an InSAR stack with consistently high correlation and computing, and then removing, a fit of the phase at each of these points with respect to elevation. A comparison with GPS truth yields a reduction of three, from a rms misfit of 5–6 to ~2 cm over time. This algorithm can be readily incorporated into InSAR processing flows without the need for outside information.

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