Landslide Susceptibility Mapping Using GIS-Based-MCDM Method In Arabdagh Forests of Iran

Document Type : Research Paper

Authors

1 PhD student, Department of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

2 Professor, Department of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 Associate Professor, Department of Forestry, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

4 Associate Professor, Department of Remote Sensing, University of KhajeNasir Tusi, Tehran, Iran

Abstract

Landslide hazards are relatively frequent in the mountainous region of Northern Iran. This paper aimed to utilize the potential application of a GIS-based multi-criteria decision-making model (MCDM) to evaluate and landslide susceptibility mapping in the Arabdagh forests, Golestan Province, northern Iran. A ground truth landslide map was prepared using aerial photographs and high-resolution satellite images interpretation, and field surveys. The landslide influencing factors maps were produced and used as thematic layers in the analysis (criteria and sub criteria). The landslide susceptibility map of the study area was produced by weighted linear combination (WLC) model based on AHP weights of factors. The resulting landslide susceptibility map was classified into five relative susceptibility zones according to the natural break method: very low, low, moderate, high, and very high with an area of 5.1%, 26.1%, 31.7%, 24.4%, and 12.8% of the total study area, respectively. The validation of the susceptibility map was performed using receiver operating characteristics (ROC) and area under the curve (AUC). The validation results showed the area under the curve of 0.852 (85.2%) with a standard error of 0.036. The susceptibility risk rate in the areas where are mainly shrub and herb lands is high and very high.

Keywords

Main Subjects

References

Abdullah, A., Nassr, S., and Ghaleeb, A. 2013. Remote Sensing and Geographic Information System for Fault Segments Mapping a Study from Taiz Area, Yemen. Journal of Geological Research. 2013, 1-12.
Abdulwahid, W.M., and Pradhan, B. 2017. Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR). Landslides. 14(3), 1057-1076.
Ahmadi, E., Dervish, A., Makhdoom, M. and Abolqasemi, S. 2005. Road routing based on environmental GIS principles. Geomatics Conference. 84, 8 pp.
Ahmed, M.F., Rogers, J.D., and Ismail, E.H. 2014. A regional level preliminary landslide susceptibility study of the upper Indus river basin. European Journal of Remote Sensing. 47(1), 343-373.
Althuwaynee, O.F., Pradhan, B., and Lee, S. 2012. Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences. 44, 120-135.
Alvioli, M., Melillo, M., Guzzetti, F., Rossi, M., Palazzi, E., Von Hardenberg, J., and Peruccacci, S. 2018. Implications of climate change on landslide hazard in Central Italy. Science of the Total Environment. 630, 1528-1543.
Ayalew, L., and 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.
Ayalew, L., Yamagishi, H., and Ugawa, N. 2004. Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides. 1(1), 73-81.
Ayalew, L., Yamagishi, H., Marui, H., and Kanno, T. 2005. Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology. 81(4), 432-445.
Basu, T., and Pal, S. 2017. Exploring landslide susceptible zones by analytic hierarchy process (AHP) for the Gish River Basin, West Bengal, India. Spatial Information Research. 25(5), 665-675.
Bednarik, M., Yilmaz, I., and Marschalko, M. 2012. Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’landslide area in south-west Slovakia. Natural Hazards. 64(1), 547-575.
Bera, A., Mukhopadhyay, B.P., and Das, D. 2019. Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim. Natural Hazards. 96(2), 935-959.
Bièvre, G., Jongmans, D., Goutaland, D., Pathier, E., and Zumbo, V. 2016. Geophysical characterization of the lithological control on the kinematic pattern in a large clayey landslide (Avignonet, French Alps). Landslides. 13(3), 423-436.
Cárdenas, N.Y., and Mera, E.E. 2016. Landslide susceptibility analysis using remote sensing and GIS in the western Ecuadorian Andes. Natural Hazards. 81(3), 829-1859.
Cevik, E., and Topal, T. 2003. GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline, Hendek (Turkey). Environmental Geology. 44(8), 949-962.
Chen, W., Panahi, M., and Pourghasemi, H.R. 2017. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena. 157, 310-324.
Claessens, L., Knapen, A., Kitutu, M.G., Poesen, J., and Deckers, J.A. 2007. Modelling landslide hazard, soil redistribution and sediment yield of landslides on the Ugandan footslopes of Mount Elgon. Geomorphology. 90(1-2), 23-35.
Committee on the Review of the National Landslide Hazards Mitigation Strategy. 2004. Partnerships for reducing landslide risk. Assessment of the National landslide hazards mitigation strategy. Board on Earth Sciences and Resources, Division on earth and life studies, The National Academic Press, Washington, p 143.
Crosta, G., and Clague, J. 2009. Dating, triggering, modeling and hazard assessment of large landslides. Geomorphology. 103, 1-4.
Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal, S., and Paudyal, P. 2008a. Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology. 102(3-4), 496-510.
Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M. Masuda, T., and Nishino, K. 2008b. GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environmental Geology. 54(2), 311-324.
Dahigamuwa, T., Yu, Q., and Gunaratne, M. 2016. Feasibility study of land cover classification based on normalized difference vegetation index for landslide risk assessment. Geosciences. 6(4), 45: https://doi.org/10.3390/geosciences6040045.
Dai, F.C., and Lee, C.F. 2002. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology. 42(3-4), 213-228.
Devkota, K.C., Regmi, A.D., Pourghasemi, H.R., Yoshida, K., Pradhan, B., Ryu, I.C., Dhital, M.R., and Althuwaynee, O.F. 2013. 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(1), 135-165.
El Jazouli, A., Barakat, A., and Khellouk, R. 2019. GIS-multi criteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenvironmental Disasters, 6(1), 1-12.
Ercanoglu, M., and Gokceoglu, C. 2002. Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Environmental Geology. 41, 720-730.
Feizizadeh, B., and Blaschke, T. 2011. Landslide risk assessment based on GIS multi-criteria evaluation: a case study in Bostan-Abad County, Iran. Journal of earth science and engineering. 1(1), 66-77.
Feizizadeh, B., and Blaschke, T. 2013. GIS-multi criteria decision analysis for landslide susceptibility mapping: comparing three methods for the Uremia lake basin, Iran. Natural Hazards. 65(3), 2105-2128.‏
Gorsevski, P.V., Jankowski, P., and Gessler, P.E. 2006a. An heuristic approach for mapping landslide hazard by integrating fuzzy logic with analytic hierarchy process. Control and Cybernetics. 35, 121-146.
Gorsevski, P.V. Gessler, P.E. Foltz, R.B., and Elliot, W.J. 2006b. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Transactions in GIS. 10(3), 395-415.
Guo, C., Montgomery, D.R., Zhang, Y., Wang, K., and Yang, Z. 2015. Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology. 248, 93-110.
Gupta, R.P., and Joshi, B.C. 1990. Landslide hazard zoning using the GIS approach—a case study from the Ramganga catchment, Himalayas. Engineering Geology. 28(1-2), 119-131.
Guzzetti, F. 2005. Review and selection of optimal geological models related to spatial information available, Action 1.14 Risk-aware is partially co-financed by the European Union under the INTEREG IIIB CADSES program, 44 pp.
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology. 31 (1–4), 181-216.
Hadmoko, D.S., Lavigne, F., and Samodra, G. 2017. Application of a semiquantitative and GIS-based statistical model to landslide susceptibility zonation in Kayangan Catchment, Java, Indonesia. Natural Hazards. 87(1), 437-468.
Hasekioğulları, G.D., and Ercanoglu, M. 2012. A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Natural Hazards. 63(2), 1157-1179.
He, Y., and Beighley R.E. 2008. GIS-based regional landslide susceptibility mapping: A case study in southern California. Earth Surface Processes and Landforms. 33, 380-393.
Holsinger, L., Parks, S.A., and Miller, C. 2016. Weather, fuels, and topography impede wildland fire spread in western US landscapes. Forest Ecology and Management. 380, 59-69.
Hong, H., Liu, J., Bui, D.T., Pradhan, B., Acharya, T.D., Pham, B.T., and Ahmad, B.B. 2018a. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena. 163, 399-413.
Hong, H., Pradhan, B. Sameen, M.I., Kalantar, B., Zhu, A., and Chen, W. 2018b. Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides. 15(4), 753-772.
Hong, H., Naghibi, S.A., Pourghasemi, H.R., and Pradhan, B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arabian Journal of Geosciences. 9(2), 112-137.
Hong, H., Chen, W. Xu, C., Youssef, A.M., Pradhan, B., and Tien Bui, D. 2017. Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto International, 32(2), 139-154.
Hung, L.Q., Batelaan, O., and De Smedt, F. 2005. Lineament extraction and analysis, comparison of Landsat-ETM and ASTER imagery, Case study: Suoimuoi tropical karst catchment, Vietnam. In Proceedings of SPIE Remote Sensing for Environmental Monitoring, GIS Applications and Geology, M. Ehlers, and U. Michel, eds. (International Society for Optics and Photonics), p 12.
Iranian Landslide Working Party (ILWP). 2007. Iranian landslides list. Forest, Rangeland, and Watershed Association, Iran. 60 p.
Jarjani, A., Akbari, H., Hosseini, S.A., and Abdi, O. 2018. Investigation of landslide danger zoning using analytical hierarchy process in GIS environment (case study: Azadshahr Kohmian forestry design). Journal of Watershed Management Research. 10(18), 197-207.
 Kamranzad, F., Mohasel Afshar, E., Mojarb, M., and Memarian, H. 2015. Landslide hazard zonation in Tehran province using data-driven and AHP methods. Scientific Quarterly Journal. 25(97), 101-114.
Kavak, K.S. 2005. Determination of palaeotectonic and neotectonic features around the Menderes Massif and the Gediz Graben (West. Turkey) using Landsat TM image. International Journal of Remote Sensing. 26, 59-78.
Kim, J.C., Lee, S. Jung, H.S., and Lee, S. 2018. Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea. Geocarto International. 33(9), 1000-1015.‏
Knapen, A., Kitutu, M.G., Poesen, J., Breugelmans, W., Deckers, J., and Muwanga, A. 2006. Landslides in a densely populated county at the footslopes of Mount Elgon (Uganda): characteristics and causal factors. Geomorphology. 73(1-2), 149-165.
Kwan, J.S., Chan, S.L., Cheuk, J.C., and Koo, R.C.H. 2014. A case study on an open hillside landslide impacting on a flexible rockfall barrier at Jordan Valley, Hong Kong. Landslides. 11(6), 1037-1050.
Le, Q.H., Van Nguyen, T.H., Do, M.D. Le, T.C.H., Nguyen, H.K., and Luu, T.B. 2018. TXT-tool 1.084-3.1: landslide susceptibility mapping at a regional scale in Vietnam. In Landslide dynamics: ISDR-ICL landslide interactive teaching tools (pp. 161-174). Springer, Cham.
Lee, S., Choi, J., and Min, K. 2004. Probabilistic landslide hazard mapping using GIS and remote sensing data at Boun, Korea. International Journal of Remote Sensing. 25(11), 2037-2052.
Lee, S., Ryu, J., Lee, M., and Won, J. 2003. Landslide susceptibility analysis using the artificial neural network at Boun, Korea. Environ Geol. 44, 820-833.
Lee, S., and Pradhan, B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4 (1), 33-41.
Mahdadi, F., Boumezbeur, A., Hadji, R., Kanungo, D.P., and Zahri, F. 2018. GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province, NE Algeria. Arabian Journal of Geosciences. 11(17), 476.
Mao, Z., Bourrier, F., Stokes, A., and Fourcaud, T. 2014. Three-dimensional modeling of slope stability in heterogeneous montane forest ecosystems. Ecological Modelling. 273, 11-22.
Mohammadi, M., and Pourghasemi, A. 2017. Prioritization of landslide-conditioning factors and its landslide susceptibility mapping using random forest new algorithm. Journal of Watershed Management Research. 8(15), 161-170.
Mohammadi, M., Pourghasemi, H.R., and Pradhan, B. 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences. 61, 221-236.
Moore, I.D., Grayson, R.B., and Ladson, A. 1991. Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrology Process. 5, 3-30.
Moos, C., Bebi, P., Graf, F., Mattli, J., Rickli, C. and Schwarz, M. 2016. How does forest structure affect root reinforcement and susceptibility to shallow landslides? Earth Surface Processes and Landforms. 41(7), 951-960.‏
Mwaniki, M.W., Moeller, M.S., and Schellmann, G. 2015. A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualizing lineaments: A case study of central region Kenya. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.‏
Nsengiyumva, J.B., Luo, G., Nahayo, L., Huang, X., and Cai, P. 2018. Landslide susceptibility assessment using spatial multi-criteria evaluation model in Rwanda. International journal of Environmental Research and Public Health. 15(2), 243.
Oh, H.J., Lee, S., and Soedradjat, G.M. 2010. Quantitative landslide susceptibility mapping at Pemalang area, Indonesia. Environmental Earth Sciences. 60(6), 1317-1328.
Oh, H.J., Park, N.W., Lee, S.S. and Lee, S. 2012. Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping. International Journal of Remote Sensing. 33(10), 3211-3231.
Oh, H.J., Lee, S., Chotikasathien, W., Kim, C.H., and Kwon, J.H. 2009. Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand. Environmental Geology. 57(3), 641-651
Pareta, K. 2004. Hydro-geomorphology of Sagar district (MP): a study through remote sensing technique. In: Proceeding in XIX MP young scientist congress, Madhya Pradesh council of science and technology (MAPCOST), Bhopal.
Parsons, R.L., and Frost, J.D. 2000. Interactive analysis of spatial subsurface data using GIS-based tool. Journal of Computing in Civil Engineering. 14(4), 215-222.
Pourghasemi, H.R., Moradi, H.R., and Aghda, S.F. 2013a. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Natural Hazards. 69(1), 749-779.
Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Mohammadi, M., and Moradi, H.R. 2013b. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arabian Journal of Geosciences. 6(7), 2351-2365.
Pourghasemi, H.R., Pradhan, B., and Gokceoglu, C. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards. 63(2), 965-996.
Pourghasemi, H.R. 2016. GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models. Scandinavian Journal of Forest Research. 31(1), 80-98.‏
Pradhan, B., and Lee, S. 2010a. Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environment Earth Science. 60(5), 1037–1054.
Pradhan, B., and Lee, S. 2010b. Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides. 7:1330.
Pradhan, B. and Youssef, A.M. 2010. Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arabian Journal of Geoscience. 3(3), 319-326.
Pradhan, B., Youssef, A.M., and Varathrajoo, R. 2010a. Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-spatial Information Science. 13(2), 93-102
Pradhan, B., Lee, S., and Buchroithner, M.F. 2010b. 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.
Pradhan, A.M.S., and Kim, Y.T. 2016. Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena. 140, 125-139.‏
Pradhan, A.M.S., and Kim, Y.T. 2014. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Natural Hazard. 72, 1189-1217.
Ramli, M.F., Yusof, N., Yusoff, M.K., Juahir, H., and Shafri, H.Z.M. 2010. Lineament mapping and its application in landslide hazard assessment: a review. Bulletin of engineering Geology and the Environment. 69(2), 215-233.
Regmi, A.D., Devkota, K.C., Yoshida, K., Pradhan, B., Pourghasemi, H.R., Kumamoto, T., and Akgun, A. 2014. Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arabian Journal of Geosciences. 7(2), 725-742.
Roodposhti, M.S., Rahimi, S., and Beglou, M.J. 2014. Promethee II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping. Natural Hazards. 73(1), 77-95.
Saaty, T.L. 1977. A scaling method for priorities in hierarchical structures. The Journal of Mathematical Psychology. 15:231–281
Saaty, T.L. 1990. How to make a decision: the analytic hierarchy process. The European Journal of Operational Research. 48(1), 9-26.
Saaty, T.L. 2005. Theory and application of the analytic network process. Pittsburg: RWS
Saklani, P. 2008. Forest fire risk zonation, a case study Pauri Garhwal, Uttarakhand, India [MSc thesis]. International Institute for Geo-information Science and Earth Observation Enschede of the Netherlands and Indian Institute of Remote Sensing (NRSA) Dehradun India; p. 71.
Sarda, V.K., and Pandey, D.D. 2019. Landslide susceptibility mapping using information value method. Jordan Journal of Civil Engineering, 13(2), 335-350.
Sengupta, A. Gupta, S., and Anbarasu, K. 2010. Rainfall thresholds for the initiation of the landslide at Lanta Khola in north Sikkim, India. Natural Hazards. 52(1), 31-42.
Sidle, R.C. and Ochiai, H. 2006. Landslides Processes, Prediction, and Land Use Water Resources Monograph 18. In Natural Resources Forum (31: 322-326).
Skilodimou, H.D., Bathrellos, G.D., Koskeridou, E., Soukis, K., and Rozos, D. 2018. Physical and anthropogenic factors related to landslide activity in the Northern Peloponnese, Greece. Land. 7(3), 85.
Stanley, T., and Kirschbaum, D.B. 2017. A heuristic approach to global landslide susceptibility mapping. Natural Hazards. 87(1), 145-164.
Vakhshoori, V., and Zare, M. 2016. Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics, Natural Hazards, and Risk. 7(5), 1731-1752.‏
Varnes, D.J. 1984. Landslide hazard zonation: a review of principles and practice. UNESCO Press, Paris.
Vergani, C., Chiaradia, E.A., and Bischetti, G.B. 2012. Variability in the tensile resistance of roots in Alpine forest tree species. Ecological Engineering. 46, 43-56
Wilson, J.P., and Gallant, J.C. (Eds.). 2000. Terrain analysis: principles and applications. John Wiley & Sons.
Xing, A.G., Wang, G., Yin, Y.P., Jiang, Y., Wang, G.Z., Yang, S.Y., and Dai, J.A. 2014. Dynamic analysis and field investigation of a fluidized landslide in Guanling, Guizhou, China. Engineering geology. 181, 1-14.
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 and Geosciences. 35(6), 1125-1138.
Youssef, A.M., Pourghasemi, H.R., Pourtaghi, Z.S., and Al-Katheeri, M.M. 2016. Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides, 13(5), 839-856.
Environmental Resources Research
Volume 10, Issue 2
July 2022
Pages 165-182

Files

Share

How to cite

Statistics