Skip to main content

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

  • Chapter
  • First Online:
  • 1179 Accesses

Abstract

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

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Abe, S. (2005). Support vector machines for pattern classification (Vol. 53). Berlin: Springer.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Bottou, L., & Lin, C.-J. (2007). Support vector machine solvers. Large Scale Kernel Machines, 301–320.

    Google Scholar 

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  • Breiman, L. (2003). RF/tools: A class of two-eyed algorithms. Paper presented at the SIAM workshop.

    Google Scholar 

  • Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1984). Classification and regression trees. Boca Raton: CRC Press.

    Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • Chow, W., Zakaria, M., Ferdaus, A., & Nurzaidi, A. (2003). Geological terrain mapping. JMG unpublished report. JMG. SWP. GS, 16, 1–42.

    Google Scholar 

  • 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 

  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

    Google Scholar 

  • Definiens, A. (2007). Definiens developer 7 reference book (pp. 21–24). München: Definiens AG.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Fang, H.-T., & Huang, D.-S. (2004). Noise reduction in LiDAR signal based on discrete wavelet transform. Optics Communications, 233(1), 67–76.

    Article  CAS  Google Scholar 

  • Foody, G. M. (2004). Thematic map comparison. Photogrammetric Engineering & Remote Sensing, 70(5), 627–633.

    Article  Google Scholar 

  • Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2–3), 131–163.

    Article  Google Scholar 

  • Galli, M., Ardizzone, F., Cardinali, M., Guzzetti, F., & Reichenbach, P. (2008). Comparing landslide inventory maps. Geomorphology, 94(3), 268–289.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Last, M., Maimon, O., & Minkov, E. (2002). Improving stability of decision trees. International Journal of Pattern Recognition and Artificial Intelligence, 16(02), 145–159.

    Article  Google Scholar 

  • Liaw, A., & Wiener, M. (2002). Classification and regression by random forest. R News, 2(3), 18–22.

    Google Scholar 

  • Lillesand, T. M., Kiefer, R. W., & Chipman, J. (2004). Remote sensing and image interpretation. New York: Wiley.

    Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • McKean, J., & Roering, J. (2004). Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Geomorphology, 57(3), 331–351.

    Article  Google Scholar 

  • Mitchell, T. M. (1997). Machine learning (Vol. 45, p. 37). Burr Ridge, IL: McGraw Hill.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. Boca Rotan: CRC Press.

    Book  Google Scholar 

  • Ohlmacher, G. C. (2007). Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology, 91(2), 117–134.

    Article  Google Scholar 

  • Olaya, V. (2009). Basic land-surface parameters. Developments in Soil Science, 33, 141–169.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Samui, P. (2008). Slope stability analysis: A support vector machine approach. Environmental Geology, 56(2), 255–267.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Team, R. C. (2013). R: A language and environment for statistical computing.

    Google Scholar 

  • Vapnik, V. N., & Vapnik, V. (1998). Statistical learning theory (Vol. 1). New York: Wiley.

    Google Scholar 

  • Varmuza, K., & Filzmoser, P. (2016). Introduction to multivariate statistical analysis in chemometrics. Boca Rotan: CRC Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Zhang, H. (2004). The optimality of naive Bayes. AA, 1(2), 3.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Biswajeet Pradhan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Pradhan, B., Alsaleh, A. (2017). A Supervised Object-Based Detection of Landslides and Man-Made Slopes Using Airborne Laser Scanning Data. In: Pradhan, B. (eds) Laser Scanning Applications in Landslide Assessment. Springer, Cham. https://doi.org/10.1007/978-3-319-55342-9_2

Download citation

Publish with us

Policies and ethics