A Remote Sensing Approach for Landslide Hazard Assessment on Engineered Slopes

Earthworks such as embankments and cuttings are integral to road and rail networks but can be prone to instability, necessitating rigorous and continual monitoring. To date, the potential of remote sensing for earthwork hazard assessment has been largely overlooked. However, techniques such as airborne laser scanning (ALS) are now ripe for addressing these challenges. This research presents the development of a novel hazard assessment strategy, combining high-resolution remote sensing with a numerical modeling approach. The research was implemented at a railway test site located in northern England, U.K.; ALS data and multispectral aerial imagery facilitated the determination of key slope stability variables, which were then used to parameterize a coupled hydrological-geotechnical model, in order to simulate slope behavior under current and future climates. A software toolset was developed to integrate the core elements of the methodology and determine resultant slope failure hazard which could then be mapped and queried within a geographical information system environment. Results indicate that the earthworks are largely stable, which is in broad agreement with the management company's slope hazard grading data, and in terms of morphological analysis, the remote methodology was able to correctly identify 99% of earthworks classed as embankments and 100% of cuttings. The developed approach provides an effective and practicable method for remotely quantifying slope failure hazard at fine spatial scales (0.5 m) and for prioritizing and reducing on-site inspection.

[1]  Sunil Sharma,et al.  SLOPE STABILITY AND STABILIZATION METHODS , 1996 .

[2]  T. Schmugge,et al.  Remote sensing in hydrology , 2002 .

[3]  Intelligent integration of multi-sensor data for risk assessment in transport corridor environments , 2009 .

[4]  K. V. Kumar,et al.  Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods , 2010 .

[5]  Stephanie Glendinning,et al.  Spatial analysis of the reliability of transport networks subject to rainfall‐induced landslides , 2008 .

[6]  Hayley J. Fowler,et al.  RainSim: A spatial-temporal stochastic rainfall modelling system , 2008, Environ. Model. Softw..

[7]  Stephanie Glendinning,et al.  High resolution earth imaging for transport corridor slope stability risk analysis , 2007 .

[8]  Myung Sagong,et al.  Feature extraction of a concrete tunnel liner from 3D laser scanning data , 2009 .

[9]  R. Soeters,et al.  Landslide hazard and risk zonation—why is it still so difficult? , 2006 .

[10]  Lee Chapman,et al.  Assessing the potential impact of climate change on transportation: the need for an interdisciplinary approach , 2010 .

[11]  P. Stott,et al.  Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000 , 2011, Nature.

[12]  J. Huisman,et al.  Temporal stability of soil moisture in various semi-arid steppe ecosystems and its application in remote sensing , 2008 .

[13]  D. Tarboton A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .

[14]  Giovanni B. Crosta,et al.  Shallow landslides in pyroclastic soils: a distributed modelling approach for hazard assessment , 2004 .

[15]  M. Eeckhaut,et al.  Tracking landslide displacements by multi-temporal DTMs: A combined aerial stereophotogrammetric and LIDAR approach in western Belgium , 2008 .

[16]  S. L. Kuriakose,et al.  Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview , 2008 .

[17]  Shiuan Wan,et al.  Development of a spatial decision support system for monitoring earthquake-induced landslides based on aerial photographs and the finite element method , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[18]  Frédéric Bretar,et al.  Full-waveform topographic lidar : State-of-the-art , 2009 .

[19]  Y. M. Cheng,et al.  Two-dimensional slope stability analysis by limit equilibrium and strength reduction methods , 2007 .

[20]  J. Seibert,et al.  On the calculation of the topographic wetness index: evaluation of different methods based on field observations , 2005 .

[21]  C. Harpham,et al.  A daily weather generator for use in climate change studies , 2007, Environ. Model. Softw..

[22]  Aniruddha Sengupta,et al.  Locating the critical failure surface in a slope stability analysis by genetic algorithm , 2009, Appl. Soft Comput..

[23]  Jonathan P. McKenna,et al.  Regional landslide-hazard assessment for Seattle, Washington, USA , 2005 .

[24]  Chenghu Zhou,et al.  Rockfall hazard analysis using LiDAR and spatial modeling , 2010 .

[25]  J. J. Aguilar,et al.  Development of a stereo vision system for non-contact railway concrete sleepers measurement based in holographic optical elements , 2005 .

[26]  D. Kawabata,et al.  Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN) , 2009 .

[27]  Angelos Amditis,et al.  Combined lane and road attributes extraction by fusing data from digital map, laser scanner and camera , 2011, Inf. Fusion.

[28]  H. A. Nefeslioglu,et al.  Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey) , 2008 .

[29]  V. Jetten,et al.  Quantitative landslide hazard assessment along a transportation corridor in southern India , 2010 .

[30]  B. McGinnity,et al.  Role of pore water pressures in embankment stability , 2004 .

[31]  John Ewen,et al.  SHETRAN: Distributed River Basin Flow and Transport Modeling System , 2000 .

[32]  Amir M. Kaynia,et al.  Identification of substructure properties of railway tracks by dynamic stiffness measurements and simulations , 2010 .

[33]  P. Reichenbach,et al.  Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy , 1999 .

[34]  Jordi Corominas,et al.  A review of assessing landslide frequency for hazard zoning purposes , 2008 .