A Supervised Object-Based Detection of Landslides and Man-Made Slopes Using Airborne Laser Scanning Data

  • Biswajeet PradhanEmail author
  • Ali Alsaleh


In recent years, airborne-derived products from light detection and ranging (LiDAR) measurements, such as high-resolution digital elevation models (DEMs), slope, curvature, shaded relief, and maps of landslides obtained from beneath dense vegetation, are becoming increasingly important for producing a detailed landslide inventory map


Support Vector Machine Random Forest LiDAR Data Landslide Susceptibility Mapping Landslide Inventory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abe, S. (2005). Support vector machines for pattern classification (Vol. 53). Berlin: Springer.Google Scholar
  2. Akcay, H. G., & Aksoy, S. (2008). Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing, 46(7), 2097–2111.CrossRefGoogle Scholar
  3. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3), 239–258.CrossRefGoogle Scholar
  4. Bignel, F., & Snelling, G. (1977). The geochronology of the main range Batholith: Cameron highlands road and Gunong Bujang Melaka. Overseas Geol Miner Resour, 47, 3–35.Google Scholar
  5. Bottou, L., & Lin, C.-J. (2007). Support vector machine solvers. Large Scale Kernel Machines, 301–320.Google Scholar
  6. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  7. Breiman, L. (2003). RF/tools: A class of two-eyed algorithms. Paper presented at the SIAM workshop.Google Scholar
  8. Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press.Google Scholar
  9. Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. Paper presented at the proceedings of the 23rd international conference on machine learning.Google Scholar
  10. Chen, L. C., Teo, T.-A., Shao, Y.-C., Lai, Y.-C., & Rau, J.-Y. (2004). Fusion of LiDAR data and optical imagery for building modeling. International Archives of Photogrammetry and Remote Sensing, 35(B4), 732–737.Google Scholar
  11. Chen, W., Li, X., Wang, Y., Chen, G., & Liu, S. (2014). Forested landslide detection using LiDAR data and the random forest algorithm: A case study of the Three Gorges, China. Remote Sensing of Environment, 152, 291–301.CrossRefGoogle Scholar
  12. Chow, W., Zakaria, M., Ferdaus, A., & Nurzaidi, A. (2003). Geological terrain mapping. JMG unpublished report. JMG. SWP. GS, 16, 1–42.Google Scholar
  13. Cobbing, E., Pitfield, P., Darbyshire, D., & Mallick, D. (1992). The granites of the SE Asian tin belt. British Geological Survey, Overseas Memoir No. 10: HMSO, London.Google Scholar
  14. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.Google Scholar
  15. Definiens, A. (2007). Definiens developer 7 reference book (pp. 21–24). München: Definiens AG.Google Scholar
  16. Duro, D. C., Franklin, S. E., & Dubé, M. G. (2012). Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests. International Journal of Remote Sensing, 33(14), 4502–4526.CrossRefGoogle Scholar
  17. Eeckhaut, M., Poesen, J., Verstraeten, G., Vanacker, V., Nyssen, J., Moeyersons, J., et al. (2007). Use of LiDAR-derived images for mapping old landslides under forest. Earth Surface Processes and Landforms, 32(5), 754–769.CrossRefGoogle Scholar
  18. Fang, H.-T., & Huang, D.-S. (2004). Noise reduction in LiDAR signal based on discrete wavelet transform. Optics Communications, 233(1), 67–76.CrossRefGoogle Scholar
  19. Foody, G. M. (2004). Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627–633.CrossRefGoogle Scholar
  20. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2–3), 131–163.CrossRefGoogle Scholar
  21. Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94(3), 268–289.CrossRefGoogle Scholar
  22. Gibril, M. B. A., Bakar, S. A., Yao, K., Idrees, M. O., & Pradhan, B. (2016). Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto International, 1–14.Google Scholar
  23. Gorum, T., Fan, X., van Westen, C. J., Huang, R. Q., Xu, Q., Tang, C., et al. (2011). Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology, 133(3), 152–167.CrossRefGoogle Scholar
  24. Grimm, R., Behrens, T., Märker, M., & Elsenbeer, H. (2008). Soil organic carbon concentrations and stocks on Barro Colorado Island—digital soil mapping using Random Forests analysis. Geoderma, 146(1), 102–113.CrossRefGoogle Scholar
  25. Guo, L., Chehata, N., Mallet, C., & Boukir, S. (2011). Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66(1), 56–66.CrossRefGoogle Scholar
  26. Hodgson, M. E., Jensen, J., Raber, G., Tullis, J., Davis, B. A., Thompson, G., et al. (2005). An evaluation of lidar-derived elevation and terrain slope in leaf-off conditions. Photogrammetric Engineering & Remote Sensing, 71(7), 817–823.CrossRefGoogle Scholar
  27. Laliberte, A. S., Rango, A., Havstad, K. M., Paris, J. F., Beck, R. F., McNeely, R., et al. (2004). Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment, 93(1), 198–210.CrossRefGoogle Scholar
  28. Last, M., Maimon, O., & Minkov, E. (2002). Improving stability of decision trees. International Journal of Pattern Recognition and Artificial Intelligence, 16(02), 145–159.CrossRefGoogle Scholar
  29. Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R News, 2(3), 18–22.Google Scholar
  30. Lillesand, T. M., Kiefer, R. W., & Chipman, J. (2004). Remote sensing and image interpretation. New York: Wiley.Google Scholar
  31. Long, N. T. (2008). Landslide susceptibility mapping of the mountainous area in A Luoi district, Thua Thien Hue province, Vietnam. Faculty of Engineering, Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Belgium.Google Scholar
  32. Martha, T. R. (2011). Detection of landslides by object oriented image analysis. University of Twente, Faculty of Geo-Information Science and Earth Observation. Enschede, The Netherlands: ITC Printing Department.Google Scholar
  33. Martha, T. R., Kerle, N., Van Westen, C. J., Jetten, V., & Kumar, K. V. (2011). Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing, 49(12), 4928–4943.CrossRefGoogle Scholar
  34. McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3), 331–351.CrossRefGoogle Scholar
  35. Mitchell, T. M. (1997). Machine learning (Vol. 45, p. 37). Burr Ridge, IL: McGraw Hill.Google Scholar
  36. Möller, M., Lymburner, L., & Volk, M. (2007). The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9(3), 311–321.CrossRefGoogle Scholar
  37. Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406.CrossRefGoogle Scholar
  38. Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. Boca Rotan: CRC Press.CrossRefGoogle Scholar
  39. Ohlmacher, G. C. (2007). Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology, 91(2), 117–134.CrossRefGoogle Scholar
  40. Olaya, V. (2009). Basic land-surface parameters. Developments in Soil Science, 33, 141–169.CrossRefGoogle Scholar
  41. Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling and Software, 25(6), 747–759.CrossRefGoogle Scholar
  42. Puissant, A., Rougier, S., & Stumpf, A. (2014). Object-oriented mapping of urban trees using Random Forest classifiers. International Journal of Applied Earth Observation and Geoinformation, 26, 235–245.CrossRefGoogle Scholar
  43. Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104.CrossRefGoogle Scholar
  44. Samui, P. (2008). Slope stability analysis: A support vector machine approach. Environmental Geology, 56(2), 255–267.CrossRefGoogle Scholar
  45. Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M., & Ellis, I. O. (2011). A ‘non-parametric’ version of the naive Bayes classifier. Knowledge-Based Systems, 24(6), 775–784.CrossRefGoogle Scholar
  46. Team, R. C. (2013). R: A language and environment for statistical computing.Google Scholar
  47. Vapnik, V. N., & Vapnik, V. (1998). Statistical learning theory (Vol. 1). New York: Wiley.Google Scholar
  48. Varmuza, K., & Filzmoser, P. (2016). Introduction to multivariate statistical analysis in chemometrics. Boca Rotan: CRC Press.Google Scholar
  49. Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., et al. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.CrossRefGoogle Scholar
  50. Xie, Z., Zhang, Q., Hsu, W., & Lee, M. L. (2005). Enhancing SNNB with local accuracy estimation and ensemble techniques. Paper presented at the international conference on database systems for advanced applications.Google Scholar
  51. Zêzere s, J. L., de Brum Ferreira, A., & Rodrigues, M Ls. (1999). The role of conditioning and triggering factors in the occurrence of landslides: A case study in the area north of Lisbon (Portugal). Geomorphology, 30(1), 133–146.CrossRefGoogle Scholar
  52. Zhang, H. (2004). The optimality of naive Bayes. AA, 1(2), 3.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Civil EngineeringUniversity Putra MalaysiaSerdangMalaysia

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