An optimized semi- automatic method for geomorphometric classification of Lut Yardangs using artificial neural etwork

Ehsani, Amir Houshang; Foroutan, Marzieh

doi:10.22059/jrwm.2014.52826

Document Type : Research Paper

Authors

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

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

https://doi.org/10.22059/jrwm.2014.52826

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

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Journal of Range and Watershed Managment

An optimized semi- automatic method for geomorphometric classification of Lut Yardangs using artificial neural etwork

References

References

Volume 67, Issue 3 - Serial Number 3
December 2014
Pages 359-380

An optimized semi- automatic method for geomorphometric classification of Lut Yardangs using artificial neural etwork

References

References

Volume 67, Issue 3 - Serial Number 3December 2014Pages 359-380

Volume 67, Issue 3 - Serial Number 3
December 2014
Pages 359-380