Journal of Range and Watershed Managment

Determining the most important geomorphometric parameters in classification of desert plains with using artificial neural networks and sensitivity analysis

Document Type : Research Paper

Authors

Gholamreza Zehtabian
Hassan Ahmadi
Aliakbar Nazari Samani
amir houshang ehsani
Mahdi Tazeh
https://doi.org/10.22059/jrwm.2017.61976
Abstract
Plains are one of the most important geomorphological units and different parameters have been considered for classification of plain areas. One of most common classifications in natural resources studies in Iran entailing different qualitative and quantitative factors is: bare plains, apandazh plain and covered plain. Such classifications are used to make plains distinguishable from one another. In this study, the geomorphometrical parameters were considered for plain classification by using artificial neural networks and sensitivity analysis. These parameters were extracted by using mathematical equations and applying the corresponding relations on digital elevation models and they are not widely used in Iran. Geomorphometric parameters that were used in this study included Percent of slope, Plan Curvature, Profile Curvature, Minimum Curvature, the Maximum Curvature, Cross sectional Curvature, Longitudinal Curvature and Gaussian Curvature. These parameters were calculated in an area of 125000 hectare and at 1500 points, and the result was compared and calibrated with ground truth map. Sampling method in this study was Latin Hyper cube that is a kind of stratified random sampling. Results of this study show that the most important geomorphometric parameters to classify desert plains include Plan Curvature and Profile Curvature that have the highest sensitivity among different plain types. The more the topography of the area reduced the more the contribution and importance of these factors for separating plain types decreased so that these parameters were most prominent in bare plains but had the lowest efficiency in covered plains.

Keywords

Artificial Neural Networks
Sensitivity analysis
Classification
Geomorphometric parameters
Plain
[1].   Ahmadi, H, 2008, Vol 2, Applied Geomorphology, University of Tehran press, Third edition.
[2].   Cooke, R.U., 1970, Morphometric analysis of pediments and associated landforms in the western Mojave Desert, California: American Journal of Science, v. 269, p. 26–38.
[3].   Ehsani, Amir Houshang, Quiel, F, 2007, A semi-automatic method for analysis of landscape elements using Shuttle Radar Topography Mission and Landsat ETM+ data, Computers & Geosciences 35 , 373–389.
[4].   Ehsani, Amir Houshang, Quiel, F, 2008, Geomorphometric feature analysis using morphometric parameterization and artificial neural networks, Geomorphology 99.
[5].   Ehsani, Amir Houshang, Quiel, F, 2008, Application of Self Organizing Map and SRTM data to characterize yardangs in the, Lut desert, Iran, Remote Sensing of Environment.
[6].   Ehsani, Amir Houshang, Quiel, F, 2009, Self-organizing maps for multi-scale morphometric feature identification using shuttle radar topography mission data, Geocarto International.
[7].   Ehsani, Amir Houshang, Quiel, F, 2010, Effect of SRTM resolution on morphometric feature identification using neural network—self organizing map, Geoinformatica.
[8].   Gilbert, G.K., 1877, Report on the Geology of the Henry Mountains, Utah: Washington, D.C., U.S. Geographical and Geological Survey of the Rocky Mountains Region, U.S. Government Printing Office ce.
[9].   Li, Zhilin. Zhu, Qing. Gold, Christopher, 2005, Digital Terrain Modeling, Principles and Methodology, CRC PRESS.
[10].   Minasny, Budiman, Alex B. Mc Bratney, 2006, A conditioned Latin hypercube method for sampling in the presence of ancillary information, Computers & Geosciences 32.
[11].   Mohammadi, j, 2007, Pedometric, vol 10, Terrain Analysis, Pelk press.
[12].   Mohammadi, j, 2009, Pedometric, vol 14, Soil digital mapping, Pelk press.
[13].   Oberlander, T.D., 1997, Slope and pediment systems, in Thomas, D.S.G., ed., Arid Zone Geomorphology: Process, Form and Change in Dry lands: John Wiley and Sons, p. 135–163.
[14].   Pelletier, Jon D, 2010, How do pediments form?: A numerical modeling investigation with comparison to pediments in southern Arizona, USA, Department of Geosciences, University of Arizona, Geological Society of America.
[15].   Pike, R.J, I.S. Evans and T. Hengl, 2009, Geomorphometry: A Brief Guide, Developments in Soil Science, Volume 33, chapture 1, Elsevier.
[16].   Rahimi Lake, H., Akbarzadeh, A. and Taghizadeh Mehrjardi, R, 2009, Development of pedo transfer functions (PTFs) to predict soil physico-chemical and hydrological characteristics in southern coastal zones of the Caspian Sea. Journal of Ecology and the Natural Environment, 1(7), 160–172.
[17].   Strudley, M.W, and Murray, A.B., 2007, Sensitivity analysis of pediment development through numerical simulation and selected geospatial query: Geomorphology, v. 88, p. 329–351, doi: 10.1016/j.geomorph.2006.12.008.
[18].   Zho, A.-Xing, 2000, Water resources research, Vol. 36, NO.3, Page 663-677.
Journal of Range and Watershed Managment
Volume 70, Issue 1
Spring 2017
Pages 197-206

  • Receive Date 25 December 2011
  • Revise Date 19 June 2017
  • Accept Date 14 November 2013
  • Publish Date 22 May 2017

Home

Guide for Authors

Submit Manuscript

Contact Us