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.
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.
Alexander, D. (2008). A brief survey of GIS in mass-movement studies, with reflections on theory and methods. Geomorphology, 94, 261–267.
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.
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.
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.
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.
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.
Burrough, P. A., & McDonell, R. A. (1998). Principles of geographical information systems (p. 190). New York: Oxford University Press.
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.
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.
Carrara, A. G., Cardinalli, M., & Guzzetti, F. (1992). Uncertainty in assessing landslide hazard and risk. ITC, 2, 1972–1983.
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.
Carrara, A., Crosta, G., & Frattini, P. (2008). Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology, 94, 353–378. b.
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.
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.
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.
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.
Claps, P., Fiorentino, M., & Oliveto, G. (1994). Informational entropy of fractal river networks. Journal of Hydrology, 187(1–2), 145–156.
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.
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.
Dai, F. C., & Lee, C. F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42, 213–228.
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.
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.
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.
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.
Ermini, L., Catani, F., & Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66, 327–343.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
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.
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.
Frattini, P., Crosta, G., Carrara, A., & Agliardi, F. (2008). Assessment of rockfall susceptibility by integrating statistical and physically-based approaches. Geomorphology, 94, 419–437.
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.
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.
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.
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.
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.
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.
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.
Hutchinson, J. N. (1968). Mass movement. In R. W. Fairbridge (Ed.), Encyclopedia of earth sciences (pp. 688–695). New York: Reinhold.
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.
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.
Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47, 982–990.
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.
Magliulo, P., Di Lisio, A., & Russo, F. (2009). Comparison of GIS-based methodologies for the landslide susceptibility assessment. Geoinformatica, 13, 253–265.
Montgomery, D. R., & Dietrich, W. E. (1989). Source areas, drainage density, and channel initiation. Water Resources Research, 25, 1907–1918.
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.
Ng, K. Y. (2006). Landslide locations and drainage network development: a case study of Hong Kong. Geomorphology, 76, 229–239.
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.
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.
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.
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.
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.
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.
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.
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.
Pradhan, B., & Lee, S. (2010). Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides, 7(1), 13–30.
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.
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.
Shagam, R. (1960). Geology of Central Aragua, Venezuela. Geological Society of America Bulletin, 71, 249–302.
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.
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.
Urbani, F., & Rodríguez, J. A. (2003). Atlas geológico de la Cordillera de la Costa, Venezuela. Caracas: Coedición UCV and FUNVISIS.
Van Westen, C. J. (2000). The modelling of landslide hazards using GIS. Surveys in Geophysics, 21, 241–255.
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.
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.
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.
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.
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
Corresponding author
Rights and permissions
About this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-016-5240-4