Skip to main content

Advertisement

Log in

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)

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

Abstract

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Akgun, A. (2011). A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides. doi:10.1007/s10346-011-0283-7.

  • Akgun, A., & Bulut, F. (2007). GIS-based landslide susceptibility for Arsin-Yomra (Trabzon, North Turkey) region. Environmental Geology, 51, 1377–1387.

    Google Scholar 

  • Aleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: Summary review and new perspectives. Bulletin of Engineering Geology and the Environment, 58, 21–44.

    Article  Google Scholar 

  • Alexander, E. D. (2005). Vulnerability to landslides. In T. Glade, M. G. Anderson, & M. J. Crozier (Eds.), Landslide risk assessment (pp. 175–198). New York: Wiley.

    Google Scholar 

  • Avşar, M. (1997) General assessment of landslides in Izmir metropolitan area. Unpublished M.S. thesis, The Graduate School of Natural and Applied Sciences, Dokuz Eylul University: Izmir, 136 p.

  • Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65, 15–31.

    Article  Google Scholar 

  • Boore, D. M., Joyner, W. B., & Fumal, T. E., (1997). Equations for estimating horizontal response spectra and peak acceleration from Western North American earthquakes: a summary of recent work. Seismological Research Letters, 68(1), 128–153.

    Google Scholar 

  • Brabb, E. E. (1984) Innovative approach to landslide hazard and risk mapping. In: Proceedings of the 4th international symposium on landslides, Vol. 1, Toronto, Canada, pp. 307–324.

  • Bunce, C. M., Cruden, D. M., & Morgenstern, N. R. (1997). Assessment of the hazard from rockfall on a highway. Canadian Geotechnical Journal, 34(3), 344–356.

    Google Scholar 

  • Cardinali, M., Reichenbach, P., Guzzetti, F., Ardizzone, F., Antonini, G., Galli, M., et al. (2002). A geomorphological approach to estimate landslide hazard and risk urban and rural areas in Umbria, central Italy. Natural Hazards Earth System Sciences, 2, 57–72.

    Article  Google Scholar 

  • Carrara, A., Cardinalli, M., Detti, R., Guzetti, F., Pasqui, V., & Reichenbach, P. (1991). GIS techniques and statistical models in evaluating landslide hazard. Earth Surface Process Landforms, 16(5), 427–445.

    Article  Google Scholar 

  • Chen, X. (2002). Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development. International Journal of Remote Sensing, 23, 107–124.

    Article  CAS  Google Scholar 

  • Chung, C. J., & Fabbri, A. G. (1999). Probabilistic prediction models for landslide hazard mapping. Photogrammetric Engineering Remote Sensing, 65, 1389–1399.

    Google Scholar 

  • Congalton, R. G., & Mead, R. A. (1983). A quantitative method to test for consistency and correctness in photointerpretation. Photogrammetric Engineering and Remote Sensing, 49, 69–74.

    Google Scholar 

  • Dai, F. C., Lee, C. F., & Ngai, Y. Y. (2002). Landslide risk assessment and management: An overview. Engineering Geology, 64(1), 65–87.

    Article  Google Scholar 

  • Das, A. M., Kumar, N. S., & Kanti, M. S. (2011). Landslide hazard and risk analysis in India at a regional scale. Disaster Advances, 4(2), 26–39.

    Google Scholar 

  • De Fries, R. S., & Chan, J. C.-W. (2000). Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 74, 503–515.

    Article  Google Scholar 

  • Deniz, A., Korkmaz, A. K., & Irfanoglu, A. (2010). Probabilistic seismic hazard assessment for İzmir, Turkey. Pure and Applied Geophysics, 167, 1475–1484.

    Article  Google Scholar 

  • Duman, T. Y., Can, T., Gokceoglu, C., Nefeslioglu, H. A., & Sonmez, H. (2006). Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey. Environmental Geology, 51, 241–256.

    CAS  Google Scholar 

  • Eastman, R. (2004). IDRISI Kilimanjaro: guide to GIS and image processing. Worcester: Clark Labs, Clark University.

    Google Scholar 

  • Einstein, H. H. (1998). Special lecture: Landslide risk assessment procedure. In C. Bonnard (Ed.), Landslides (pp. 1075–1090). Rotterdam: Balkema.

    Google Scholar 

  • Erdoğan, B. (1990). Stratigraphy and tectonic evolution of İzmir–Ankara zone between İzmir and Seferihisar. TAPG Bulletin, 2(1), 1–20 (in Turkish).

    Google Scholar 

  • Fell, R. (1994). Landslide risk assessment and acceptable risk. Canadian Geotechnical Journal, 31, 261–272.

    Article  Google Scholar 

  • Fell, R., & Hartford, D. (1997). Landslide risk management. In D. M. Cruden & R. Fell (Eds.), Landslide risk management (pp. 51–109). Rotterdam: Balkema.

    Google Scholar 

  • Galli, M., & Guzzetti, F. (2007). Landslide vulnerability criteria: A case study from Umbria, central Italy. Environmental Management, 40, 649–664.

    Article  Google Scholar 

  • Gao, H.-X., & Yin, K.-L. (2011). GIS-based spatial prediction of landslide hazard risk. Journal of Natural Disasters, 20(1), 31–36.

    Google Scholar 

  • General Directory of Meteorological Services of Turkey (2010). http://www.dmi.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?m=IZMIR. Accessed 16 Dec 2010.

  • Glade, T., & Crozier, M. J. (2005). A review of scale dependency in landslide hazard and risk analysis. In T. Glade, M. G. Anderson, & M. J. Crozier (Eds.), Landslide risk assessment (pp. 75–138). Chichester: Wiley.

    Google Scholar 

  • Gokceoglu, C., & Sezer, E. (2009). A statistical assessment on international landslide literature (1945–2008). Landslides, 6, 345–351.

    Article  Google Scholar 

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

    Google Scholar 

  • Gulkan, P., & Kalkan, E., (2002). Attenuation modeling of recent earthquakes in Turkey. Journal of Seismology, 6(3), 397–409.

    Google Scholar 

  • Guzzetti, F. (2000). Landslide fatalities and evaluation of landslide risk in Italy. Engineering Geology, 58, 89–107.

    Article  Google Scholar 

  • Guzzetti, F., Reichenbach, P., & Ghigi, S. (2004). Rockfall hazard and risk assessment in the Nera River Valley, Umbria Region, central Italy. Environmental Management, 34, 191–208.

    Article  Google Scholar 

  • Guzzetti, F., Stark, C. P., & Salvati, P. (2005). Evaluation of flood and landslide risk to the population of Italy. Environmental Management, 36, 15–36.

    Article  Google Scholar 

  • Guzzetti, F., Galli, M., Reichenbach, P., Ardizzone, F., & Cardinali, M. (2006). Landslide hazard assessment in the Collazzone area, Umbria, central Italy. Natural Hazards Earth System Sciences, 6, 115–131.

    Article  Google Scholar 

  • Guzzetti, F., Reichenbach, P., Ardizzone, F., Cardinali, M., & Galli, M. (2009). Landslide hazard assessment, vulnerability estimation and risk evaluation. An example from the Collazzone Area (Central Umbria, Italy). Geogr Fis Dinam Qual, 32, 183–192.

    Google Scholar 

  • Hearn, G. J., & Griffiths, J. S. (2001). Landslide hazard mapping and risk assessment. Geological Society, London, Engineering Geology Special Publications, 18, 43–52.

    Article  Google Scholar 

  • Huang, X., & Jensen, R. (1997). A machine-learning approach to automated knowledge-base building for remote sensing image analysis with GIS data. Photogrammetric Engineering and Remote Sensing, 63, 1185–1194.

    Google Scholar 

  • Jaiswal, P., van Westen, C. J., & Jetten, V. (2010). Quantitative landslide hazard assessment along a transportation corridor in southern India. Engineering Geology, 116(3–4), 236–250.

    Article  Google Scholar 

  • Jaiswal, P., Van Westen, C. J., & Jetten, V. (2011a). Quantitative estimation of landslide risk from rapid debris slides on natural slopes in the Nilgiri hills, India. Natural Hazards and Earth System Science, 11(6), 1723–1743.

    Article  Google Scholar 

  • Jaiswal, P., van Westen, C. J., & Jetten, V. (2011b) Quantitative assessment of landslide hazard along transportation lines using historical records. Landslides, 8(3), 279–291.

    Google Scholar 

  • Jensen, J. R. (2000). Introductory digital image processing: A remote sensing perspective. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Kandilli Observatory and Earthquake Research Institute (KOERI) (2008). http://www.koeri.boun.edu.tr. Accessed 12 Apr 2008.

  • Kavzoglu, T., & Mather, P. M. (2003). The use of back-propagating artificial neural networks in land cover classification. International Journal of Remote Sensing, 24, 4907–4938.

    Article  Google Scholar 

  • Keefer, D. K. (1984). Landslides caused by earthquakes. Geological Society of America Bulletin, 95(2), 406–421.

    Google Scholar 

  • Kıncal, C. (2005) Engineering geological evaluation of geological units outcrop in and around the Izmir City Centre with the help of geographical information systems and remote sensing techniques. Unpublished Ph.D. thesis, The Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir, 342 p. (in Turkish).

  • Kıncal, C., & Koca, M. Y. (2009). A proposed method for drawing the great circle representing dip angle and strike changes. Environmental and Engineering Geoscience, 15, 145–165.

    Article  Google Scholar 

  • Kıncal, C., Akgün, A., & Koca, M. Y. (2009). Landslide susceptibility assessment in the Izmir (West Anatolia, Turkey) city center and its near vicinity by the logistic regression method. Environmental Earth Sciences, 59, 745–756.

    Article  Google Scholar 

  • Koca, M. Y. (1995) Slope stability assessment of the abandoned andesite quarries in and around the Izmir city centre. Unpublished Ph.D. thesis, Dokuz Eylul University, Izmir 430 p.

  • Kouli, M., Loupasakis, C., Soupios, P., & Vallianatos, F. (2010). Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece. Natural Hazards, 52(3), 599–621.

    Article  Google Scholar 

  • Lee, S. (2005). Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. International Journal of Remote Sensing, 26(7), 1477–1491.

    Article  Google Scholar 

  • Lee, S. (2007). Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea. Earth Surface Processes and Landforms, 32(14), 2133–2148.

    Article  Google Scholar 

  • Lee, S., & Pradhan, B. (2006). Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Journal Earth System Science, 115(6), 661–672.

    Article  Google Scholar 

  • Lee, S., Choi, J., & Min, K. (2004). Probabilistic landslide hazard mapping using GIS and remote sensing data at Boeun, Korea. International Journal of Remote Sensing, 25, 2037–2052.

    Article  Google Scholar 

  • M.T.A. (2000). Geological map of Turkey, 1,25.000-scaled Izmir sheet. Izmir: MTA.

    Google Scholar 

  • Malczewski, J. (1999). GIS and multicriteria decision analysis. New York: Wiley.

    Google Scholar 

  • Nandi, A., & Shakoor, A. (2009). A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110, 11–20.

    Article  Google Scholar 

  • Nefeslioglu, H. A., & Gokceoglu, C. (2011). Probabilistic risk assessment in medium scale for rainfall induced earthflows: Catakli catchment area (Cayeli, Rize, Turkey). Mathematical Problems in Engineering, Article ID 280431.

  • Nefeslioglu, H. A., Gokceoglu, C., & Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97, 171–191.

    Article  Google Scholar 

  • Nefeslioglu, H. A., Gokceoglu, C., Sonmez, H., & Gorum, T. (2011). Medium-scale hazard mapping for shallow landslide initiation: The Buyukkoy catchment area (Cayeli, Rize, Turkey). Landslides. doi:10.1007/s10346-011-0267-7.

  • Newman, M. C., & Strojan, C. L. (1998). Risk assessment: Logic and measurement. Chelsea: Ann Arbor.

    Google Scholar 

  • Oh, H.-J., & Lee, S. (2010). Cross-validation of logistic regression model for landslide susceptibility mapping at Geneoung areas, Korea. Disaster Advances, 3(2), 44–54.

    Google Scholar 

  • Peters-Guarin, G., Mccall, M. K., & Van Westen, C. (2011). Coping strategies and risk manageability: Using participatory geographical information systems to represent local knowledge. Disasters. doi:10.1111/j.1467-7717.2011.01247.x.

  • Prabu, S., & Ramakrishnan, S. S. (2009). Combined use of socio economic analysis, remote sensing and GIS data for landslide hazard mapping using ANN. Journal of the Indian Society of Remote Sensing, 37(3), 409–421.

    Article  Google Scholar 

  • Pradhan, B. (2010a). Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45(10), 1244–1256.

    Article  CAS  Google Scholar 

  • Pradhan, B. (2010b). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38(2), 301–320.

    Article  Google Scholar 

  • Pradhan, B. (2011). Manifestation of an advanced fuzzy logic model coupled with geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environmental and Ecological Statistics, 18(3), 471–493. doi:10.1007/s10651-010-0147-7.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010a). Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modeling Software, 25(6), 747–759.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010b). Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environmental Earth Sciences, 60(5), 1037–1054.

    Article  Google Scholar 

  • Pradhan, B., & Youssef, A. M. (2010). Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geosciences, 3(3), 319–326.

    Article  Google Scholar 

  • Pradhan, B., Lee, S., Mansor, S., Buchroithner, M. F., Jallaluddin, N., & Khujaimah, Z. (2008). Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Applied Remote Sensing, 2, 1–11.

    Google Scholar 

  • Pradhan, B., Lee, S., & Buchroithner, M. F. (2010). Remote sensing and GIS-based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogrammetrie, Fernerkundung, Geoinformation, 2010(1), 17–32. doi:10.1127/1432-8364/2010/0037.

    Article  Google Scholar 

  • Pradhan, B., Mansor, S., Pirasteh, S., & Buchroithner, M. (2011). Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. International Journal of Remote Sensing, 32(14), 4075–4087.

    Article  Google Scholar 

  • RADIUS. (1997). Risk assessment tools for diagnosis of urban areas against seismic disaster Izmir earthquake master program. Istanbul: Bogazici University Kandilli Observatory.

    Google Scholar 

  • Remondo, J., Bonachea, J., & Cendrero, A. (2008). Quantitative landslide risk assessment and mapping on the basis of recent occurrences. Geomorphology, 94(3–4), 496–507.

    Article  Google Scholar 

  • Rogan, J., & Chen, D. M. (2004). Remote sensing technology for mapping and monitoring land cover and land-use change. Progress in Planning, 61(4), 301–325.

    Article  Google Scholar 

  • Schuster, R. L. (1996). Socioeconomic significance of landslides. In A. K. Turner & R. L. Schuster (Eds.), Landslides—investigation and mitigation. Transp. Res. Board Spec. Report 247 (pp. 12–35). Washington, DC: National Research Council.

    Google Scholar 

  • Sener, B., Suzen, M. L., & Doyuran, V. (2006). Landfill site selection by using geographic information systems. Environmental Geology, 49, 376–388.

    Google Scholar 

  • Soeters, R., & Van Westen, C. J. (1996). Slope instability recognition analysis and zonation. In K. T. Turner & R. L. Schuster (Eds.), Landslides: Investigation and mitigation. Transportation Research Board National Research Council, special report no 247 (pp. 129–177). Washington, DC: National Research Council.

    Google Scholar 

  • Sterlacchini, S., Frigerio, S., Giacomelli, P., & Brambilla, M. (2007). Landslide risk analysis: A multi-disciplinary methodological approach. Natural Hazards Earth System Science, 7, 657–675.

    Article  Google Scholar 

  • Sunar, F., & Kaya, S. (1997). An assessment of geometric accuracy of remotely sensed images. International Journal of Remote Sensing, 18, 3069–3074.

    Article  Google Scholar 

  • Tang, C., Zhu, J., & Qi, X. (2011). Landslide hazard assessment of the 2008 Wenchuan earthquake: A case study in Beichuan area. Canadian Geotechnical Journal, 48(1), 128–145.

    Article  Google Scholar 

  • Van Westen, C. J., Van Asch, T. W. J., & Soeters, R. (2006). Landslide hazard and risk zonation—why is it still so difficult? Bulletin of Engineering Geology and the Environment, 65, 167–184.

    Article  Google Scholar 

  • Van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide, susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102, 112–131.

    Article  Google Scholar 

  • Vandine, D. F., Moore, G., Wise, M., Vanbuskirk, C., & Gerath, R. (2004). Technical terms and methods. In M. Wise, G. Moore, & D. Vandine (Eds.), Landslide risk case studies in forest development planning and operations. B.C. Ministry of Forests Forest Science Program, abstract of land management handbook 56 (pp. 13–26). Victoria: B.C. Ministry of Forests, Forest Science Program.

    Google Scholar 

  • Varnes, D. J. (1978). Slope movement types and processes. In R. L. Schuster & R. J. Krizek (Eds.), Landslides analysis and control Special report, vol 176 (pp. 12–33). New York: Transportation Research Board, National Academy of Sciences.

    Google Scholar 

  • Varnes, D. J. (1984). Landslide hazard zonation: a review of principles and practice (p. 63). Paris: UNESCO.

    Google Scholar 

  • Wu, Y.-P., Yin, K.-L., & Jiang, W. (2009). Early warning of landslide risk in Yongjia County, Zhejiang Province. Journal of Natural Disasters, 18(2), 124–130.

    Google Scholar 

  • Yilmaz, I. (2009). Landslide susceptibility using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat-Turkey). Computers and Geosciences, 35(6), 1125–1138.

    Google Scholar 

  • Zezere, J. L., Garcia, R. A. C., Oliveira, S. C., & Reis, E. (2008). Probabilistic landslide risk analysis considering direct costs in the area north of Lisbon. Geomorphology, 94(3–4), 467–495.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Dr. M. Yalcın Koca from Dokuz Eylul University and Prof. Dr. Candan Gokceoglu from Hacettepe University for their constructive comments to this study and Mr. Raşit Özkan for his help during the field surveying.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aykut Akgun.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Akgun, A., Kıncal, C. & Pradhan, B. Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess 184, 5453–5470 (2012). https://doi.org/10.1007/s10661-011-2352-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-011-2352-8

Keywords

Navigation