Document Type : Research Paper

Authors

1 Associate Professor, Environment Faculty, University of Tehran, Iran

2 Ph.D Candidate, Geography, Calgary University, Canada

Abstract

In this study the land surface in western half of hyper-arid Lut desert, in south east of Iran, which is covered by Yardangs, a worldwide typical landform for Aeolian erosion, were classified by Self Organizing Maps (SOM) method. In the first step by using Digital Elevation Model with 10 m resolution and Matlab software, 22 morphometric parameters were calculated based on derivative of the surface elevation with first, second and third orders. In the second step most affective parameters for classification and the optimum number of classes were found through utilizing Optimum Index Factor and Davies Bouldin Index. Finally SOM classification was performed on seven morphometric parameters to result in seven classes. The results showed that most appropriate parameters in classification of area are plan curvature, rotor, hypsometric Integral, total accumulation curvature, slope steepness, extreme curvature and mean curvature. The study area were divided to seven classes including saddle valley, Concave ellipsoid, Gentle slope corridor, shoulder with concave slope, shoulder with convex slope, ridge, corridor channels. Sensitivity analysis results revealed that the most sensitive parameters are rotor, mean curvature and hypsometric Integral. Also the results of Jeffreys-Matusita Distance illustrated that parameter pair hypsometric integral / extreme curvature has the most ability in separation of classes in this area. Comparison of the separated classes with the landforms on aerial photographs confirms our classification results.

Keywords

[1] Bue, B.D. and Stepinski, T.F. (2006). Automated classification of landforms on Mars. Computers & Geosciences, 32, 604-661.
[2] Dikau, R. (1989). The application of a digital relief model to landform analysis in geomorphology. In: Raper, J. (Ed.), Three Dimensional Applications in Geographical Information Systems. Taylor & Francis, London, pp. 51-77.
[3] Darvishzadeh., A. (1991). Geology of Iran, Amirkabir Press, Tehran, Iran.
[4] Davies, D.L. and Bouldin, D.W. (1979). A cluster separation measure. IEEE Trans. Patt. Anal. Machine Intelligence, 1, 224-227.
[5] Ehsani, A.H. and Quiel, F. (2008). Geomorphometric feature analysis using morphometric parameterization and artificial neural networks. Geomorphology, 99, 1-12.
[6] Ehsani, A.H. and Quiel, F. (2008). Application of self organizing map and SRTM data to characterize yardangs in the Lut desert, Iran. Remote Sensing of Environment, 112, 3284-3294.
[7] Ehsani, A.H. and Quiel, F. (2009). Self-organizing maps for multi-scale morphometric feature identification using shuttle radar topography mission data. Geocarto International, 24, 335-355.
[8] Ehsani, A.H. et al. (2010). Effect of SRTM resolution on morphometric feature identification using neural network-self organizing map. Geoinformatica, 14, 405-424.
[9] Evans, I.S. (1972). General geomorphology, derivatives of altitude and descriptive statistics. In R.J. Chorley (Ed.), Spatial Analysis in Geomorphology (pp. 17-90). London: Methuen & Co. Ltd.
[10] Florinsky, I.V. (1998). Accuracy of local topographic variables derived from digital elevation models. International Journal of Geographical Information Science, 12, 47-61.
[11] Florinsky, I.V. (1998). Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Progress in Physical Geography, 22, 33-60.
[12] Florinsky, I.V. (2002). Errors of signal processing in digital terrain modelling. International Journal of Geographical Information Science, 16, 475-501.
[13] Florinsky, I.V. (2009). Computation of the third-order partial derivatives from a digital elevation model. International Journal of Geographical Information Science, 23, 2: 213-231.
[14] Frankel, K.L. and Dolan, J.F. (2007). Characterizing arid region alluvial fan surface roughness with airborne laser swath mapping digital topographic data. Journal of Geophysical Research-Earth Surface, 112, F02025.
[15] Grebby, S (2010). Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data. Remote Sensing of Environment, 114, 713-724.
[16] Hengel. T. and Router, H. (2008). Geomorphometry, Concepts, Software, Applications. Elsevier.
[17] Ji, C.Y. (2000). Land-use classification of remotely sensed data using Kohonen Self- Organizing Feature Map neural networks. Photogrammetric Engineering and Remote Sensing, 66, 1451-1460.
[18] Kohonen, T. (2001). Self Organizing Maps. 3rd Ed. Springer, New York.
[19] Prima, O.D.A., Echigo, A., Yokoyama, R. and Yoshida, T. (2006). Supervised landform classification of Northeast Honshu from DEM-derived the maticmaps. Geomorphology, 78, 373-386.
[20] Saux, E., et al.( 2004). A New Approach for a Topographic Feature-Based Characterization of Digital Elevation Data. GIS’04, 73-81.
[21] Shary, P.A., Sharaya, L.S. and Mitusov, A.V. (2002). Fundamental quantitative methods of land surface analysis. Geoderma, 107, 1-32.
[22] Wood, J. (1996). The Geomorphological Characterization of Digital Elevation Models. Ph.D. Thesis, Department of Geography, University of Leicester, UK.
[23] Zevenbergen, L.W. and Thorne, C.R. (1987). Quantitative analysis of land surface topography. Earth Surface Processes and Landforms, 12, 47-56.