Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping

Abstract:

Article Preview

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

Info:

Periodical:

Main Theme:

Edited by:

R. Varatharajoo, E. J. Abdullah, D. L. Majid, F. I. Romli, A. S. Mohd Rafie and K. A. Ahmad

Pages:

442-447

Citation:

B. Pradhan et al., "Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping", Applied Mechanics and Materials, Vol. 225, pp. 442-447, 2012

Online since:

November 2012

Export:

Price:

$38.00

[1] A. Corsini., F. Cervi., A. Daehne., F. Ronchetti, Coupling geomorphic field observation and LIDAR derivatives to map complex landslides, in: Malet JP, Remaıˆtre A, Bogaard T (Eds), Landslides processes from geomorphologic mapping to dynamic modelling, Proceedings of the landslide processes conference., Strasbourg, (2009).

[2] B. Pradhan, Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia, Advances in space research. 45(10) (2010) 1244-1256.

DOI: https://doi.org/10.1016/j.asr.2010.01.006

[3] B. Pradhan, Application of an advanced fuzzy logic model for landslide susceptibility analysis, International Journal of Computational Intelligence Systems. 3 (2010) 370-381.

DOI: https://doi.org/10.1080/18756891.2010.9727707

[4] B. Pradhan, Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches, Journal of the Indian Society of Remote Sensing. 38(2) (2010) 301-320.

DOI: https://doi.org/10.1007/s12524-010-0020-z

[5] B. Pradhan., S. Lee, Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia, Landslides. 7(1) (2010) 13-30.

DOI: https://doi.org/10.1007/s10346-009-0183-2

[6] B. Pradhan., S. Lee., M.F. Buchroithner, Remote sensing and GIS-based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model, Photogrammetrie, Fernerkundung, Geoinformation 2010. 1 (2010).

DOI: https://doi.org/10.1127/1432-8364/2010/0037

[7] B. Pradhan, Manifestation of an advanced fuzzy logic model coupled with geoinformation techniques for landslide susceptibility analysis, Environmental and Ecological Statistics. 18 (2011a) 471-493.

[8] B. Pradhan, Use of GIS based fuzzy relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia, Environmental Earth Sciences. 63 (2011b) 329-349.

DOI: https://doi.org/10.1007/s12665-010-0705-1

[9] B. Pradhan., S. Mansor., S. Pirasteh and M.F. Buchroithner, Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model, Internal Journal of Remote Sensing. 32(14) (2011) 1-4087.

DOI: https://doi.org/10.1080/01431161.2010.484433

[10] C.Z. Qin., A.X. Zhu., T. Pei., B.L., T. Scholten., T. Behrens, An approach to computing topographic wetness index based on maximum downslope gradient, Precision Agriculture. 12(1) (2011) 32-43.

DOI: https://doi.org/10.1007/s11119-009-9152-y

[11] F. Guzzetti., P. Reicenbach., M. Cardinali., M. Galli and F. Ardizzone, Probabilistic landslide hazard assessment at the basin scale, Geomorphology. 72 (1–4) (2005) 272-299.

DOI: https://doi.org/10.1016/j.geomorph.2005.06.002

[12] I.D. Moore., R.B. Grayson and A.R. Landson, Digital terrain modelling: A review of hydrological, geomorphological, and biological applications, Hydrol. Process. 5 (1991) 3–30.

DOI: https://doi.org/10.1002/hyp.3360050103

[13] J.J. Oh., B. Pradhan, Application of a neuro-fuzzy model to landslide susceptibility mapping in a tropical hilly area, Computers & Geosciences. 37 (9) (2011) 1264-1276.

DOI: https://doi.org/10.1016/j.cageo.2010.10.012

[14] M. Jaboyedoff., T. Oppikofer., A. Abellán., M.H. Derron., A. Loye., R. Metzger, Use of LiDAR in landslide investigations: a review, Natural Hazards. 1-24 (2010).

DOI: https://doi.org/10.1007/s11069-010-9634-2

[15] M. van den Eeckhaut., J. Poesen., G. Verstraeten., V. Vanacker., J. Nyssen., J. Moeyersons., L.P.H. van Beek., L. Vandekerckhove, Use of LIDAR-derived images for mapping old landslides under forest, Earth Surf Proc Land. 32 (2007) 754-769.

DOI: https://doi.org/10.1002/esp.1417

[16] S. Lee., B. Pradhan, Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia, Earth system science. 115(6) (2006) 661–672.

DOI: https://doi.org/10.1007/s12040-006-0004-0

[17] W.H. Ting, In Proceedings Symposium on Geotechnical Aspects of Mass and Material Transportation, Bangkok, 1984, pp.119-128.