Effects of the Spatial Resolution of Digital Elevation Models and Their Products on Landslide Susceptibility Mapping

  • Biswajeet PradhanEmail author
  • Maher Ibrahim Sameen


Landslides are among the destructive natural disasters that cause significant damage to human life and properties worldwide. Numerous researchers have attempted to provide an understanding of landslide causes and related problems. An important and simple analysis method that has been used in landslide studies is landslide susceptibility mapping/modeling (LSM). LSM is fundamental to hazard and risk assessments, and it is widely used by governments for planning land use and strategic projects. LSM requires landslide conditioning factors and landslide inventories, which can be acquired using remote sensing and field surveying techniques. The output of LSM is a map that shows the degree of landslide susceptibility of an area.


Normalize Difference Vegetation Index Landslide Susceptibility Landslide Inventory Topographic Wetness Index Landslide Susceptibility Assessment 
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  1. Alin, A. (2010). Multicollinearity. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3), 370–374. Google Scholar
  2. Althuwaynee, O. F., Pradhan, B., & Lee, S. (2016). A novel integrated model for assessing land-slide susceptibility mapping using CHAID and AHP pair-wise comparison. International Journal of Remote Sensing, 37(5), 1190–1209.Google Scholar
  3. Chalkias, C., Kalogirou, S., & Ferentinou, M. (2014). Landslide susceptibility, Peloponnese Peninsula in South Greece. Journal of Maps, 10(2), 211–222.Google Scholar
  4. Chang, K. T., Dou, J., Chang, Y., Kuo, C. P., Xu, K. M., & Liu, J. K. (2016). Spatial resolution effects of digital terrain models on landslide susceptibility analysis. ISPRS-International Ar-chives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 33–36. Google Scholar
  5. Chung, C. J. F., & Fabbri, A. G. (2003). Validation of spatial prediction models for landslide hazard mapping. Natural Hazards, 30(3), 451–472.Google Scholar
  6. Eker, A. M., Dikmen, M., Cambazoğlu, S., Düzgün, Ş. H., & Akgün, H. (2015). Evaluation and comparison of landslide susceptibility mapping methods: A case study for the Ulus district, Bartın, northern Turkey. International Journal of Geographical Information Science, 29(1), 132–158.Google Scholar
  7. Guri, P. K., & Patel, R. C. (2015). Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling. Environmental monitoring and assessment, 187(6), 1–25.Google Scholar
  8. Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2006). Estimating the quality of landslide susceptibility models. Geomorphology, 81(1), 166–184.Google Scholar
  9. Huang, J., Zhou, Q., & Wang, F. (2015). Mapping the landslide susceptibility in Lantau Island, Hong Kong, by frequency ratio and logistic regression model. Annals of GIS, 21(3), 191–208.Google Scholar
  10. Intarawichian, N., & Dasananda, S. (2011). Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environmental Earth Sciences, 64(8), 2271–2285.Google Scholar
  11. Lee, S. (2005). Application and cross-validation of spatial logistic multiple regression for land-slide susceptibility analysis. Geosciences Journal, 9(1), 63–71.Google Scholar
  12. Lee, S., Choi, J., & Woo, I. (2004). The effect of spatial resolution on the accuracy of landslide susceptibility mapping: A case study in Boun, Korea. Geosciences Journal, 8(1), 51–60.Google Scholar
  13. Mahalingam, R., Olsen, M. J., & O’Banion, M. S. (2016). Evaluation of landslide susceptibility mapping techniques using lidar-derived conditioning factors (Oregon case study). Geomatics, Natural Hazards and Risk, 7(6), 1–24.Google Scholar
  14. Mathew, J., Jha, V. K., & Rawat, G. S. (2009). Landslide susceptibility zonation mapping and its validation in part of Garhwal Lesser Himalaya, India, using binary logistic regression analysis and receiver operating characteristic curve method. Landslides, 6(1), 17–26.Google Scholar
  15. Meinhardt, M., Fink, M., & Tünschel, H. (2015). Landslide susceptibility analysis in central Vietnam based on an incomplete landslide inventory: Comparison of a new method to calculate weighting factors by means of bivariate statistics. Geomorphology, 234, 80–97.Google Scholar
  16. Mukherjee, S., Mukherjee, S., Garg, R. D., Bhardwaj, A., & Raju, P. L. N. (2013). Evaluation of topographic index in relation to terrain roughness and DEM grid spacing. Journal of Earth System Science, 122(3), 869–886.Google Scholar
  17. Norusis, M. J. (2006). SPSS 15.0 guide to data analysis. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  18. Oh, H. J., Park, N. W., Lee, S. S., & Lee, S. (2012). Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping. International Journal of Remote Sensing, 33(10), 3211–3231.Google Scholar
  19. Park, N. W. (2015). Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environmental Earth Sciences, 73(3), 937–949.Google Scholar
  20. Pourghasemi, H. R., Jirandeh, A. G., Pradhan, B., Xu, C., & Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. Journal of Earth System Science, 122(2), 349–369.Google Scholar
  21. Pradhan, A. M. S., & Kim, Y. T. (2014). Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeokri Creek, South Korea. Natural hazards, 72(2), 1189–1217.Google Scholar
  22. Pradhan, A. M. S., & Kim, Y. T. (2016). Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena, 140, 125–139. Google Scholar
  23. Pradhan, A. M. S., Kang, H. S., Lee, S., & Kim, Y. T. (2016). Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea. Geocarto International, 1–22.Google Scholar
  24. Pradhan, B. (2010). Application of an advanced fuzzy logic model for landslide susceptibility analysis. International Journal of Computational Intelligence Systems, 3(3), 370–381.Google Scholar
  25. Pradhan, B., Lee, S. (2010). Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland. Malaysia. Landslides, 7(1), 13–30.Google Scholar
  26. Qin, C. Z., Bao, L. L., Zhu, A. X., Wang, R. X., & Hu, X. M. (2013). Uncertainty due to DEM error in landslide susceptibility mapping. International Journal of Geographical Information Science, 27(7), 1364–1380.Google Scholar
  27. Quinn, P. E. (2014). Landslide susceptibility in sensitive clay in eastern Canada: Some practical considerations and results in development of an improved model. International Journal of Image and Data Fusion, 5(1), 70–96.Google Scholar
  28. Raman, R., & Punia, M. (2012). The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley, Western Himalaya, India. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 6(3), 145–161.Google Scholar

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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity Putra MalaysiaSerdangMalaysia

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