Skip to main content

Advertisement

SpringerLink
Log in

An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, Northern Thailand

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

A landslide susceptibility assessment was conducted for the Yom River catchment, North Thailand, using a weight of evidence approach. In total, 1630 landslide events were detected using remote sensing techniques. An integrated workflow based on robust statistical threshold criteria was applied to reveal the landslide-controlling factors that can be utilised on a regional scale. Initially, approximately 15 different factors were considered within this study. After a sensitivity and plausibility analysis, a final susceptibility map was generated based on the three most essential factors, which fulfilled the statistical requirements of independence, prediction power and plausibility. The final map was subdivided into five susceptibility zones using quantitative classification. The map provides a suitable and reliable starting point for further detailed mass movement analysis in the Yom River Basin and can be used to support strategic spatial planning measures on a regional scale.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Agterberg FP, Cheng Q (2002) Conditional independence test for weight of evidence modeling. Nat Resour Res 11(4):249–255

    Article  Google Scholar 

  • Aleotti P, Chowdhury RP (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Env 58:21–44

    Article  Google Scholar 

  • Armas I (2012) Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania. Nat Hazards 60:937–950

    Article  Google Scholar 

  • Atkinson PM, Massari R (1998) Generalized linear modelling of landslide susceptibility in the Central Apennines, Italy. Comput Geosci 24:373–385

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Backhaus K, Erichson B, Plinkeand W, Weiber R (2011) Multivariate Analysemethoden—Eine anwendungsorientierte Einführung. Springer, Berlin

    Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists: modelling with GIS. Pergamon Press, Ottawa

    Google Scholar 

  • Bonham-Carter GF, Agterberg FP, Wright DF (1989) Weights of evidence modelling: a new approach to mapping mineral potential. Stat Appl in Earth Sci 89(9):171–183

    Google Scholar 

  • Bunopas S (1981) Paleogeographic history of western Thailand and adjacent part of Southeast Asia. A plate tectonics interpretation. Geological Survey Paper 5 (Spec. Iss.): 810 pp.

  • Chung AJF, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogram Eng Remote Sens 65(12):1389–1399

    Google Scholar 

  • Chung AJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472

    Article  Google Scholar 

  • Clauss G, Ebner H (2002) Grundlagen der Statistik für Psychologen. Pädagogen und Soziologen, Harri Deutsch, Frankfurt

    Google Scholar 

  • Crewson P (2006) Applied Statistics Handbook. v. 1.2. http://www.acastat.com. Accessed 24 June 2014.

  • Cruden ADM, Varnes DJ (1996) Landslide types and processes. In: Turner AK, Schuster RL (eds) Landslides investigation and mitigation. Transportation Research Board, US National Research Council. Special Report, Washington DC, pp 36–75

    Google Scholar 

  • Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

    Article  Google Scholar 

  • Erener A, Düzgün HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? Env Earth Sci 66:859–877

    Article  Google Scholar 

  • Fan D, Cui X, Yuan D, Wang J, Yang J, Wang S (2011) Weight of evidence method and its applications and development. Procedia Environ Sci 11:1412–1418

    Article  Google Scholar 

  • Fuchs M, Torizin J, Kühn F (2014) The effect of DEM resolution on the computation of the factor of safety using infinite slope model. Geomorphology 224:16–26

    Article  Google Scholar 

  • GADM (2014) Database of Global Administrative Areas. http://www.gadm.org/. Accessed 06 May 2014.

  • Geofabrik (2014) Open street map. http://www.geofabrik.de/index.html. Accessed 10 May 2014.

  • Greenbaum D, Tutton M, Bowker MR, Browne TJ, Buleka J, Grally KB, Kuna G, McDonald AJW et al. (1995) Rapid methods of landslide hazard mapping: Papua New Guinea case study. Technical Report WC/95/27 Overseas Geology Series. United Kingdom, Nottingham

  • Guzzetti F, Carrarra A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, central Italy. Geomorphology 31:181–216

    Article  Google Scholar 

  • Guzzetti F, Reichenbach P, Cardinali M, Galli M, Ardizzone F (2005) Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72:272–299

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jaervis A (2005) Very high resolution interpolated climate surfaces for gobal land areas. Int J Climatol 25:1965–1978

    Article  Google Scholar 

  • Huete A, Didan H, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83:195–213

    Article  Google Scholar 

  • Jenson SK, Domingue JO (1988) Extracting topographic structure from digital elevation data for geographic information system analysis. Photogramm Eng Remote Sens 54(11):1593–1600

    Google Scholar 

  • Lee S, Evangelista DG (2006) Earthquake-induced landslide-susceptibility mapping using an artificial neural network. Nat Hazards Earth Syst 6:687–695

    Article  Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yougin, Korea. J. Environ Geol 40:1095–1113

    Article  Google Scholar 

  • Lee S, Choi J, Min K (2002) Landslide susceptibility analysis and verification using the Bayesian probability model. Environ Geol 43:120–131

    Article  Google Scholar 

  • Martínez-Casasnovas JA, Klaasse A, Nogués J, Ramos MC (2008) Comparison between land suitability and actual crop distribution in an irrigation district of the Ebro valley (Spain). Span J Agric Res 6(4):700–713

    Article  Google Scholar 

  • Mathew J, Jha VK, Rawat GS (2007) Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley Uttarakhand. Curr Sci 92(5):628–638

    Google Scholar 

  • NASA (2013) Land Processes Distributed Active Archive Centre (LP DAAC), MODIS. http://modis.gsfc.nasa.gov/. Accessed 14 March 2014.

  • NCDC (2014) National Climate Data Centre. http://www.ncdc.noaa.gov/oa/ncdc.html. Accessed 14 March 2014.

  • Piyasin S (1974) Geological Map of Thailand 1: 250,000, sheet Uttaradit (NE 47–11). DRM Rep. Invest. 16: 68 pp. Bangkok, [Thai with English summary].

  • Pradhan B, Lee S (2010a) Landslide susceptibility assessment and factor effect analysis: back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7:13–30

    Article  Google Scholar 

  • Sarkar S, Kanungo DP, Mehrotra GS (1995) Landslide hazard zonation: a case study in Garhwal Himalaya, India. Mt Res Dev 15:301–309

    Article  Google Scholar 

  • Soil Survey Division Staff (1993) Soil survey manual. USDA, SCS, Agric. Handb. 18. U.S. Gov. Print. Office, Washington, DC

    Google Scholar 

  • Tagil S, Jennes J (2008) GIS-based automated landform classification and topographic, land cover and geologic attributes of landforms around the Yazoren Polje, Turkey. J Appl Sci 8:910–921

    Article  Google Scholar 

  • Tape TG (2012) Interpreting Diagnostic Tests. University of Nebraska Medical Center.http://gim.unmc.edu/dxtests/. Accessed 10 March 2014

  • Tarboton DG, Bras RL, Rodriguez-Iturbe I (1991) On the extraction of channel networks from digital elevation data. Hydrol Process 5:81–100

    Article  Google Scholar 

  • Teerarungsigul S (2006) Remote sensing for geohazard study in case of landslide and field investigation in northern part of Thailand. Dissertation, SUT, Nakhon Ratchasima

    Google Scholar 

  • Torizin J (2012) Landslide Susceptibility Assessment Tools for ArcGIS 10 and their Application. In: Proceedings of 34th IGC, Brisbane, 5–10 August 2012, p. 730

  • ASTER GDEM Validation Team (2011) ASTER Global Digital Elevation Model Version 2 – Summary of Validation Results. METI & NASA, 27 p

  • Van Den Eeckhaut M, Reichenbach P, Guzzetti F, Rossi M, Poesen J (2009) Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9:507–521

    Article  Google Scholar 

  • Van Westen CJ (2000) The modelling of landslide hazards using GIS. Surv Geophys 21:241–255

    Article  Google Scholar 

  • Van Westen CJ, Rengers N (1997) Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rund 86:404–414

    Article  Google Scholar 

  • Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Article  Google Scholar 

  • Van Westen CJ, van Asch TWJ, Soeters R (2006) Landslide hazard and risk zonation: why is it still so difficult? Bulletin of engineering geology and the environment IAEG, 65 (2006) 2, pp. 167–184

  • Varnes DJ, IAEG Commission on Landslides (1984) Landslide hazard zonation—a review of principles and practice. UNESCO, Paris

    Google Scholar 

  • Weiss A (2001) Topographic position and landforms analysis. Poster Presentation ESRI User Conference, San Diego, CA

    Google Scholar 

  • Wolfart R (1987) Geology of Amphoe Sop Prap (Sheet 1:50,000 No. 4844-I) and Amphoe Wang Chin (Sheet 1:50,000 No. 4944–4). Geol Jahrb 65:1–55

    Google Scholar 

  • Wood J (1996) The geomorphological characterisation of digital elevation models. Department of Geography, University of Leicester, Dissertation

    Google Scholar 

  • Yin KL, Yan TZ (1988) Statistical prediction models for slope instability of metamorphosed rocks. In: Bonnard C (ed) Proceedings 5th International Symposium on Landslides, Lausanne. Switzerland. Balkema, Rotterdam, pp 1269–1272

    Google Scholar 

Download references

Acknowledgments

We sincerely thank the Royal Golden Jubilee PhD Program (RGJ No. PHD/0140/2545) and Thailand Research Fund as well as the DAAD of Germany for funding this research. Additionally, we are especially grateful to the Suranaree University of Technology (Thailand) and Federal Institute for Geosciences and Natural Resources (Germany) for their technical support.

Author information

Authors and Affiliations

  1. Geological Bureau, Department of Mineral Resources, Bangkok, 14000, Thailand

    Suree Teerarungsigul

  2. Federal Institute for Geosciences and Natural Resources, 30655, Hanover, Germany

    Jewgenij Torizin & Michael Fuchs

  3. formerly Federal Institute for Geosciences and Natural Resources, Am Feld 7, 15738, Zeuthen, Germany

    Friedrich Kühn

  4. School of Geotechnology, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand

    Chongpan Chonglakmani

Authors
  1. Suree Teerarungsigul
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jewgenij Torizin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michael Fuchs
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Friedrich Kühn
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chongpan Chonglakmani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jewgenij Torizin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Teerarungsigul, S., Torizin, J., Fuchs, M. et al. An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, Northern Thailand. Landslides 13, 1151–1165 (2016). https://doi.org/10.1007/s10346-015-0659-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-015-0659-1

Keywords

Search

Navigation