The combination of spectral and spatial data in zoning of landslide susceptibility (Case study: Sangorchay reservoir)

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Research Paper 01/02/2015
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The combination of spectral and spatial data in zoning of landslide susceptibility (Case study: Sangorchay reservoir)

Mohmmad Hoessin Fathi, Kazem Khohdel, Amir Shoreh Kandi, Zahra Ashrafifeini, Mohammad Ali Khaliji
J. Bio. Env. Sci.6( 2), 515-527, February 2015.
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Abstract

Maps of landslide susceptibility is one of the most important necessary tools for the environmental programmers and people who make decisions especially in mountainous areas. The main goal of this research is to evaluate and use the data and methods of far-distance evaluation such as satellite images and also to use Multiple Criteria Decision Making models in Zoning of landslide susceptibility. (ANP) Analytic Network process is among the models of preparing map of landslide susceptibility this model has kept the capabilities and advantages of AHP model and has fulfilled the related problems and therefore in recent years it has been used more than AHP and has actually replaced it. In this research, we have zoned the landslide susceptibility in Sangor Chay. For this research, we have used 17 natural and human parameters (rainfall, distance to fault, distance to river, drainage density, slope degree and direction, land usage, vegetation coverage and etc.) Choice, decision and ENVI are among the tools that are used for pair comparisons, providing data and performing the model. Evaluation results show that 76 percent of landslides that have been occurred in the region, belong to “dangerous” and “extremely dangerous” classes. According to this, Data and parameters resulted from far-distance models and also multiple criteria Decision making models, are likely to be suitable for forecasting the landslide susceptibility.

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Abedini M. 2012. Landslide hazard zonation Givi Chay catchment based on Analytical Hierarchy Process, Department of Physical Geography Research Project 12(53), 34-46.

Amalnik M, Ansarinajad A, Ansarinajad S, Mirnargezi S. 2010. Finding a causal relationship between ratings and critical success factors of the project and implementation of information systems using a combination of ANP and DEMATEL fuzzy. Journal of Industrial Engineering 44(2), 212-195.

Ayalew L, Yamagishi H, Ugawa N. 2004. Landslide susceptibility mapping using GIS based weighted linear combination. The case in Tsugawa area of Agano River. Niigata Prefecture, Japan. Landslide 1, 73–81.

Baeza C, Corominas J. 2001. Assessment of shallow landslide susceptibility by means of multivariate statistical techniques. Earth Surface Processes and Landforms 26, 1251–1263.

Bowen WM. 1990. Subjective judgments and data environment analysis in site selection, Computer, Environment and Urban Systems 44, 133-144.

Changa KF, Chiangb CM, Chouc PC. 2007. Adapting aspects of GB Tool 200`—searching for suitability in Taiwan, Building and Environment 42, 310–316.

Çimren E, Çatay B, Budak E. 2007. Development of a machine tool selection system using AHP, International Journal of Advanced Manufacturing Technology 35, 363–376.

Das I, Sahoo S, van Westen C, Stein A, Hack R. 2010. Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology 114, 627–637.

Deering D, Rouse J, Haas R, Schell J. 1975. Measuring forage production of grazing using from landsat MSS data. In: Tenth International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, 12(44), 1169–1178.

Dey PK, Ramcharan EK. 2000. Analytic hierarchy process helps select site for limestone quarry expansion in Barbados. Journal of Environmental Management 5(20), 118-132.

Ebadi Najad A, Yamani M, Magsoodi M, Samad SH. 2007. Evaluating performance of fuzzy logic in determining the ability of landslides, Journal of Watershed Management Engineering Science 1(2), 68-89.

Fathi MH. 2011. Geomorphologic analysis of military location using GIS & RS (Case Western slopes of Mount Sahand), Master’s thesis, natural Geography, Geomorphology, University of Tabriz.

Feayzneya S, Klarstaqy A, Ahmadi H, Safaii M. 2004. The study of main factors influencing the occurrence of landslides and landslide hazard zonation (Case Study: SHirin River catchment dam Tajan), Journal of Iranian Natural Resources 57(1), 3-20.

Gharahi HR, Bahman B, Mohsen S. 2011. Mapping the phenomena of landslide susceptibility by using bivariate analysis and hierarchical statistical model in the Alborz Dam. Journal of Earth Sciences 21(81), 93-100.

Gorum T, Gonencgil B, Gokceoglu C, Nefeslioglu HA. 2008. Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NWTurkey). Natural Hazards 9(46), 323–351.

Grabs T, Seibert J, Laudon H. 2007. Modelling spatial patterns of saturated areas: a comparison of the topographic wetness index and a distributed model. Geophysical Research 7(28), 1607-7962.

Guzzetti F, Carrara A, Cardinali M, Reichenbach P. 1999. Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study. Central Italy. Geomorphology 5(31), 181–216.

Hossein Zadeh M, sarvaty M, Mansouri A, Mirbagheri B, Khezri S. 2009. Zoning risk of mass movements using a logistic regression model, Journal of Geology 3(7), 27-37.

Lee H. 2009. Selection of technology acquisition mode using the analytic network process. Mathematical and Computer Modeling 11(49), 1274-1282.

Lee S, Min K. 2001. Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology 12(40), 1095–1113.

Mahdifar MR, Fatemi S. 1997. The application of fuzzy sets in landslide zoning and description provided by the computer system. Proceedings of the Second Conference on Landslide and reduce the damage of the International Institute of Seismology and Earthquake Engineering 2(9), 55-32.

Malczewski J. 1999. GIS and multi criteria decision analysis. John & Sons Inc 134-146.

Marjanović M, Kovačević M, Bajat B, Voženílek V. 2011. Susceptibility assessment using SVM machine learning algorithm, Engineering Geology 8(42), 225–234.

Martha TR, Van Westen CJ, Kerle N, Jetten V, Kumar KV. 2013. Landslide hazard and risk assessment using semi-automatically created Landslide inventories, Geomorphology 39(84), 139– 150.

Mathew J, Jha VK, Rawat GS. 2007. Weights of evidence modeling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Current Sci 92(5), 628-638.

Mirsaneey SR, Kardan R. 2002. An analytical approach of the characteristics of the landslide, Proceedings of the First Conference on Environmental Engineering Geology, printing, Tarbiat Moallem University, Tehran 83-84.

Mohammady M, Pourghasemi HR, Pradhan B. 2012. Landslide susceptibility mapping at Golestan Province, Iran: Acomparison Between frequency ratio, Dempster–Shafer, and weights-of-evidence models, Journal of Asian Earth Sciences 61, 221–236.

Moore ID, Burch GJ. 1986. Sediment transport capacity of sheet and rill flow: application of unit stream power theory. Water Resource 22, 1350– 1360.

Moqimi E, Yamani M, Rahimi Herovabadi S. 2013. Evaluation of landslide hazard zonation in roudbar by using the network analysis, quantitative research, the first year 4, 103-118.

Nazmfar H, Beheshti Javid E, Fathi MH. 2013. Flooding and flood zonation of the fuzzy logic model (case study: gorichay catchment), Second International Conference on Environmental Hazards, Tehran University Khwarizmi 1-9.

Nefeslioglu HA, 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.

Nefeslioglu HA, San BT, Gokceoglu C, Duman TY. 2012. An assessment on the use of Terra ASTER L3A data in landslide susceptibility mapping. International Journal of Applied Earth Observation and Geoinformation 14, 40–60.

Neyazi Y, Ekhtesasi MR, Mokhtari MH. 2010. Evaluating the performance of the bivariate statistical model predicts landslide in Ilam dam basin, Watershed Management’s Engineering Journal 10, 9-20.

Nik Andish N. 2006. View of the importance of mass movements in Iran, College of Agriculture 7(155), 84-95.

Ninomiya Y. 2002. Mapping quartz, carbonate minerals and mafic–ultramafic rocks using remotely sensed multispectral thermal infrared aster data. In: Proceedings of SPIE, USA, 191–202.

Piacentinia D, Troiani F, Soldati M, Notarnicola C, Savelli D, Schneiderbauer S, Strada C. 2012. Statistical analysis for assessing shallow-landslide susceptibility in South Tyrol (south-eastern Alps, Italy), Geomorphology 151–152, 196–206.

Pradhan B. 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences 51, 350–365.

Ramadan B, Ibrahimi H. 2009. Landslides and remedial measures, Journal of use planning environment, 2(7), 110-118.

Roering JJ, Kirchner JW, Dietrich WE. 2005. Characterizing Structural and Lithology Controls on Deep-seated Landsliding: Implications for Topographic Relief and Landscape Evolution in the Oregon Coast Range, Geological Society of America Bulletin 117, 654-668.

Rowan L, Mars J. 2003. Lithologic mapping in the Mountain Pass, California area using Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) data. Remote Sensing of Environment 84, 350–366.

Saaty T. 2006. Decision making with the analytic network process: economic, political, social and technological applications with benefits, opportunities, costs and risks. New York: Springer.

Saaty T. 1980. The analytic hierarchy process: planning, priority setting, and resource allocation. New York; London: McGraw-Hill International Book Co.

Schmidt F, Persson A. 2003. Comparison of DEM data capture and topographic wetness indices. Precision Agriculture 4, 179–192.

Sorensen R, Zinko U, Seibert J. 2006. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrology and Earth System Sciences Discussions 2, 1807–1834.

Süzen ML, Kaya BS. 2012. Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. International Journal of Digital Earth 5, 338–355.

Tommaso I, Rubinstein N. 2007. Hydrothermal alteration mapping using ASTER data in the Infiernillo porphyry deposit, Argentina. Ore Geology Reviews 32, 275–290.

Van der Meer FD, van der Werff HMA, van Ruitenbeek FJA, Hecker CA, Bakker WH, Noomen MF, van der Meijde M, Carranza EJM, Smeth JBd, Woldai T. 2012. Multi- and hyperspectral geologic remote sensing: a review. International Journal of Applied Earth Observation and Geoinformation 3(14), 112–128.

Van Western CJ. 2002. Use of weights of evidence modeling for landslide susceptibility mapping, pp.21.

Wilson JP, Gallant JC. 2000. Digital terrain analysis. In:Wilson, J.P., Gallant, J.C.(Eds.), Terrain Analysis. John Wiley & Sons, New York, 1–27.

Yamani M, Ahmedabadi A, Zare GH. 2012. Algorithm using support vector machines in landslide hazard zonation (Case Study: Darakeh catchment), Journal of Geography and environmental hazards 23(46), 125- 142.

Zabardast E. 2010. Application of analytic network process (ANP) in Urban and Regional Planning, Journal of Art-Architecture 8(41), 39-25.