Skip to main content

Advertisement

Log in

Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Adediran, A. O., Parcharidis, I., Poscolieri, M., & Pavlopoulos, K. (2004). Computer-assisted discrimination of morphological units on north-central Crete (Greece) by applying multivariate statistics to local relief gradients. Geomorphology, 58, 357–370.

    Article  Google Scholar 

  • Alexander, D. (2008). A brief survey of GIS in mass-movement studies, with reflections on theory and methods. Geomorphology, 94, 261–267.

    Article  Google Scholar 

  • Ardiansyah Prima, O. D., Echigo, A., Yokohama, R., & Yoshida, T. (2006). Supervised landform classification of Northeast Honshu from DEM-derived thematic maps. Geomorphology, 78, 373–386.

    Article  Google Scholar 

  • Bai, S., Lü, G., Wang, J., Zhou, P., & Ding, L. (2010). GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China. Environmental Earth Sciences, 62(1), 139–149.

    Article  Google Scholar 

  • Böhner, J. (2004). Regionalisierung bodenrelevanter Klimaparameter für das Niedersächsische Landesamt für Bodenforschung (NLfB) und die Bundesanstalt für Geowissenschaften und Rohstoffe (BGR). Arbeitshefte Boden, 4, 17–66.

    Google Scholar 

  • Bolongaro-Crevenna, A., Torres-Rodriguez, V., Sorani, V., Framed, D., & Ortiz, M. A. (2005). Geomorphometric analysis for characterizing landforms in Morelos State, Mexico. Geomorphology, 67, 407–422.

    Article  Google Scholar 

  • Budetta, P., Santo, A., & Vivenzio, F. (2008). Landslide hazard mapping along the coastline of the Cilento region (Italy) by means of a GIS-based parameter rating approach. Geomorphology, 94, 340–352.

    Article  Google Scholar 

  • Burrough, P. A., & McDonell, R. A. (1998). Principles of geographical information systems (p. 190). New York: Oxford University Press.

    Google Scholar 

  • Calvello, M., Cascini, L., & Mastroianni, S. (2013). Landslide zoning over large areas from a sample inventory by means of scale-dependent terrain units. Geomorphology, 182, 33–48.

    Article  Google Scholar 

  • Can, T., Nefeslioglu, H., Gokceoglu, C., Sonmez, H., & Duman, T. Y. (2005). Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses. Geomorphology, 72, 250–271.

    Article  Google Scholar 

  • Carrara, A. G., Cardinalli, M., & Guzzetti, F. (1992). Uncertainty in assessing landslide hazard and risk. ITC, 2, 1972–1983.

    Google Scholar 

  • Carrara, A., Guzzetti, F., Cardinali, M., & Reichenbach, P. (1999). Use of GIS technology in the prediction and monitoring of landslide hazard. Natural Hazards, 20, 117–135. b.

    Article  Google Scholar 

  • Carrara, A., Crosta, G., & Frattini, P. (2008). Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology, 94, 353–378. b.

    Article  Google Scholar 

  • Chacón, J., Irigara, E., Fernández, E. T., & El Hamdouni, R. (2006). Engineering geology maps: landslides and geographical information systems. Bulletin of Engineering Geology and the Environment, 65, 341–411.

    Article  Google Scholar 

  • Chau, K. T., & Chan, J. E. (2005). Regional bias of landslide data in generating susceptibility maps using logistic regression: case of Hong Kong Island. Landslides, 2, 280–290. doi:10.1007/s10346-005-0024-x.

    Article  Google Scholar 

  • Choi, J., Oh, H.-J., Lee, H.-J., Lee, C., & Lee, S. (2012). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering Geology, 124, 12–23.

    Article  Google Scholar 

  • Chung, C. J. (2006). Using likelihood ratio functions for modeling the conditional probability of occurrence of future landslides for risk assessment. Computers and Geosciences, 32, 1052–1068.

    Article  Google Scholar 

  • Claps, P., Fiorentino, M., & Oliveto, G. (1994). Informational entropy of fractal river networks. Journal of Hydrology, 187(1–2), 145–156.

    Google Scholar 

  • EPOCH (European Community Programme (1993). Temporal occurrence and forecasting of landslides in the European Community, Flageollet, J. C. (ed.), 3 volumes. Contract no. 90 0025.

  • Corominas, J., Van Westen, C., Frattini, P., Cascini, L., Malet, J. P., Fotopoulou, S., Catani, F., van den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., Winter, M. G., Pastor, M., Ferlisi, S., Tofani, V., Herva’S, J., & Smith, J. T. (2014). Recommendations for the quantitative analysis of landslide risk. Bulletin of Engineering Geology and the Environment, 73(2), 209–263.

    Google Scholar 

  • D’Amato Avanzi, G., Giannecchini, R., & Puccinelli, A. (2004). The influence of the geological and geomorphological settings on shallow landslides. An example in a temperate climate environment: the June 19, 1996 event in northwestern Tuscany (Italia). Engineering Geology, 73, 215–228.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., Dhital, M. R., & Althuwaynee, O. F. (2012). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Natural Hazards, 65, 135–165.

    Article  Google Scholar 

  • Dewitte, O., Chung, C., Cornet, Y., Daoudi, M., & Demoulin, A. (2010). Combining spatial data in landslide reactivation susceptibility mapping: a likelihood ratio-based approach in W Belgium. Geomorphology, 122, 153–166.

    Article  Google Scholar 

  • Douglas, G. B., Mcivor, I. R., Manderson, A. K., Koolaard, J. P., Todd, M., Braaksma, S., & Gray, R. A. J. (2013). Reducing shallow landslide occurrence in pastoral hill country using wide-spaced trees. Land Degradation and Development, 24, 103–114.

    Article  Google Scholar 

  • Duman, T. Y. (2005). Interactive comment on “Landslide susceptibility mapping of Cekmece área (Istanbul, Turkey) by conditional probability” by T. Y. Duman et al. Hydrology and Earth System Sciences Discussions, 2, 229–231p.

    Article  Google Scholar 

  • Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66, 327–343.

    Article  Google Scholar 

  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    Article  Google Scholar 

  • Federici, P. R., Puccinelli, A., Cantarelli, E., Casarosa, N., D’Amato Avanzi, G., Falaschi, F., Giannecchini, R., Pochini, A., Ribolini, A., Bottai, M., Salvati, N., & Testi, C. (2006). Multidisciplinary investigations in evaluating landslide susceptibility. An example in the Serchio River valley (Italia). Quaternary Internacional, 171–172, 52–63.

    Google Scholar 

  • Felicísimo, Á., Cuartero, A., Remondo, J., & Quirós, E. (2012). Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. doi:10.1007/s10346-012-0320-1.

    Google Scholar 

  • Frattini, P., Crosta, G., Carrara, A., & Agliardi, F. (2008). Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology, 94, 419–437.

    Article  Google Scholar 

  • Gorsevski, P. V., Gessler, P. E., & Jankowski, P. (2003). Integrating a fuzzy k-means classification and a Bayesian approach for spatial prediction of landslide hazard. Journal of Geographical Systems, 5, 223–251.

    Article  Google Scholar 

  • Gorsevski, P. V., Gessler, P. E., Boll, J., Elliot, W. J., & Foltz, R. B. (2006). Spatially and temporally distributed modeling of landslide susceptibility. Geomorphology, 80, 178–198.

    Article  Google Scholar 

  • Greco, R., Sorriso-Valvo, M., & Catalano, E. (2007). Logistic regression analysis in the evaluation of mass movements susceptibility: the Aspromonte case study. Calabria, Italy, Engineering Geology, 89, 47–66.

    Article  Google Scholar 

  • Gupta, V., & Sah, M. P. (2008). Spatial variability of mass movements in the Satluj Valley, Himachal Pradesh during 1990 ∼ 2006. Journal of Materials Science, 5, 38–51.

    Google Scholar 

  • Guzzetti, F., Aleotti, B., Malamud, D., & Turcotte, D.L. (2003). Comparison of three landslide events in central and northern Italy In: Jansà A. & Romero R. (eds.), Proceedings 4th Plinius Conference on Mediterranean Storms, Mallorca, Spain, Universitat de Illes Baleares, CD-ROM. ISBN 84-7632-792-7. 4p

  • 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 

  • He, S., Pan, P., Dai, L., Wang, H., & Liu, J. (2012). Application of kernel-based Fisher discriminant analysis to map landslide susceptibility in the Qinggan River delta, Three Gorges, China. Geomorphology, 171–172, 30–41.

    Article  Google Scholar 

  • Hovius, N., Stark, C. P., Tutton, M. A., & Abbott, L. D. (1998). Landslide-driven drainage network evolution in a pre-steady-state mountain belt: Finisterre Mountains, Papua New Guinea. Geology, 26(12), 1071–1074.

    Article  Google Scholar 

  • Hutchinson, J. N. (1968). Mass movement. In R. W. Fairbridge (Ed.), Encyclopedia of earth sciences (pp. 688–695). New York: Reinhold.

    Google Scholar 

  • Hutchinson, M. F. (1989). A new procedure for gridding elevation and. stream line data with automatic removal of spurious pits. Journal of Hydrology (Amsterdam), 106, 211–232.

    Article  Google Scholar 

  • Kavzoglu, T., Sahin, E. K., & Colkesen, I. (2014). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides, 11, 425–439.

    Article  Google Scholar 

  • Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.

    Article  CAS  Google Scholar 

  • Lee, S., Ryu, J. H., & Kim, I. S. (2007). Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea. Landslides, 4, 327–338.

    Article  Google Scholar 

  • Magliulo, P., Di Lisio, A., & Russo, F. (2009). Comparison of GIS-based methodologies for the landslide susceptibility assessment. Geoinformatica, 13, 253–265.

    Article  Google Scholar 

  • Montgomery, D. R., & Dietrich, W. E. (1989). Source areas, drainage density, and channel initiation. Water Resources Research, 25, 1907–1918.

    Article  Google Scholar 

  • Moore, I. D., Grayson, R. B., & Landson, A. R. (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes, 5, 3–30.

    Article  Google Scholar 

  • Ng, K. Y. (2006). Landslide locations and drainage network development: a case study of Hong Kong. Geomorphology, 76, 229–239.

    Article  Google Scholar 

  • O’Callaghan, J. F., & Mark, D. M. (1984). The extraction of drainage networks from digital elevation data. Computer Vision, Graphics and Image Processing, 28, 323–44.

    Article  Google Scholar 

  • Ohlmacher G., & Davis, J. C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering Geology 69, 331–343. www.elsevier.com/locate/enggeo. Accessed 14 Jan 2012

  • Ozdemir, A., & Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180–197.

    Article  Google Scholar 

  • Palamakumbure, D., Flentje, P., & Stirling, D. (2015). Consideration of optimal pixel resolution in deriving landslide susceptibility zoning within the Sydney Basin, New South Wales, Australia. Computers & Geosciences, 82, 13–22.

    Article  Google Scholar 

  • Parise, M. (2001). Landslide mapping techniques and their use in the assessment of the landslide hazard. Physics and Chemistry of the Earth, 26(9), 697–703.

    Google Scholar 

  • Park, S., Choi, C., Kim, B., & Kim, J. (2013). Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environmental Earth Sciences, 68, 1443–1464.

    Article  Google Scholar 

  • Pineda, M. C., Elizalde, G., & Viloria, J. (2011a). Determinación de áreas susceptibles a deslizamientos en un sector de la cordillera de la costa central de Venezuela. Interciencia, 36(5), 370–377.

    Google Scholar 

  • Pineda, M. C., Elizalde, G., & Viloria, J. (2011b). Relación suelo-paisaje en un sector de la cuenca del Río Caramacate, Aragua, Venezuela. Revista de la Facultad de Agronomía. UCV, 37(1), 27–37.

    Google Scholar 

  • Pineda, M. C., Viloria, A., & Viloria, J. (2012). Aplicación de regresión logística y redes bayesianas para evaluar susceptibilidad a deslizamientos en montañas. Suelos Ecuatoriales, 42(1), 23–27.

    Google Scholar 

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

    Article  Google Scholar 

  • Remondo, J., González-Díez, A., Díaz de Terán, J. R., & Cendrero, A. (2003). Landslide susceptibility models utilising spatial data analysis techniques. A case study from the lower Deba valley, Guipúzcoa (Spain). Natural Hazards, 30(3), 267–279.

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings 3rd ERTS Symposium, 1, 48–62.

    Google Scholar 

  • Shagam, R. (1960). Geology of Central Aragua, Venezuela. Geological Society of America Bulletin, 71, 249–302.

    Article  CAS  Google Scholar 

  • Shrestha, D. P., & Zinck, J. A. (1999). Land degradation assessment using geographic information system: a case study in the middle mountain region of the Nepalese Himalaya. The Netherlands: International Institute for Aerospace Survey and Earth Sciences (ITC). 19pp.

    Google Scholar 

  • Tangestani, M. (2003). Landslide susceptibility mapping using the fuzzy gamma operation in a GIS, Kakan catchment area, Iran, Shiraz University, Faculty of Sciences Dept, of Earth Sciences, Shiraz, Iran. Landslide & Soil Erosion. 6p.

  • Tarboton, D. G., Bras, R. L., & Rodriguez-Iturbe, I. (1991). On the extraction of channel networks from digital elevation data. Hydrologic Processes, 5(1), 81–100.

    Article  Google Scholar 

  • Urbani, F., & Rodríguez, J. A. (2003). Atlas geológico de la Cordillera de la Costa, Venezuela. Caracas: Coedición UCV and FUNVISIS.

    Google Scholar 

  • Van Westen, C. J. (2000). The modelling of landslide hazards using GIS. Surveys in Geophysics, 21, 241–255.

    Article  Google Scholar 

  • Varnes, D. J. (1978). Slope movement types and processes, in landslides analysis and control. In R. L. Schuster & R. J. Krizek (Eds.), Transportation research board, special report 176 (pp. 11–35). Washington, DC: National Academy of Science.

    Google Scholar 

  • Varnes, D. J. (1984). Landslide hazard zonation: a review of principles and practice. The International Association of Engineering Geology Commission on Landslides and Other Mass Movements. Natural Hazards, 3–63 (Paris, France. ISBN 92-3- 101895-7)

  • Viloria-Botello, A., Chang, C., Pineda, M.C., & Viloria-Rendón, J. (2012). Estimation of susceptibility to landslides using neural networks based on the FALCON-ART. 11th International Conference on Machine Learning and Applications ICMLA December 12–15, Boca Raton, Florida, USA.

  • Wilson, J. P., & Gallant, J. C. (2000). Terrain analysis principles and applications, Wiley, Toronto, p 479 Working Party on World Landslide Inventory, 1993, a suggested method for describing the activity of a landslide. Bulletin of International Association of Engineering Geology, 47, 53–57.

    Google Scholar 

  • Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35, 1125–1138.

    Article  Google Scholar 

  • Zhou, C. H., Lee, C. F., Li, J., & Xu, Z. W. (2002). On the spatial relationship between landslides and causative factors on Lantau Island, Hong Kong. Geomorphology, 43, 197–207.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy; the Venezuelan Organic Law for Science and Technology (LOCTI); and the Council of Scientific and Humanistic Development (CDCH) of the Universidad Central de Venezuela and the Universidad de Lleida (Catalonia, Spain) for financial support and fellowships.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. C. Pineda.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pineda, M.C., Viloria, J. & Martínez-Casasnovas, J.A. Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region. Environ Monit Assess 188, 255 (2016). https://doi.org/10.1007/s10661-016-5240-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5240-4

Keywords

Navigation