Forest road planning using artificial neural network and GIS

Document Type : Research Paper

Authors

1 M.Sc. student, Dept. of Forestry, University of Tehran, Karaj, I. R. Iran

2 Associate Prof., Dept. of Forestry, University of Tehran, Karaj, I. R. Iran

3 Associate Prof., Amir Kabir University, Tehran, I. R. Iran

4 Prof., Dept. of Forestry, University of Tehran, Karaj, I. R. Iran

Abstract

Forest roads are constructed to facilitate forest protection, reforestation, logging operations and maximizing the value of forest products. Therefore forest roads are key infrastructures in the development of the region. This study aims to plan forest road network using artificial neural network and GIS regarding forest road technical principles. First the criteria were chosen using Delphi method and then they were weighted regarding their importance in road planning. After that the criteria were combined with corresponding weighs to achieve suitability map based on the degree of suitability for road allocation. Value and coordinates of each pixel were extracted by ENVI software and were normalized in the range of 0-1 for modeling by MATLAB software. In this study two neural networks were used for modeling, including multilayer perceptron (MLP) and radial-bases functions (RBF). The neural networks estimated suitability of different pixels in other districts based on the Patom district results. Using an ArcView GIS extension, PEGGER, two forest road networks were planned. The results showed that MLP provides better ability for estimating suitability of pixels for road passage in comparison with RBF with the R2 of 0.994. A linear regression was also used to compare the results of the proposed neural networks. The results revealed that neural network improves the results in comparison with the linear regression and results showed that the second road alternative was optimum network with regard to the unit cost.

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Iranian Journal of Forest
Volume 10, Issue 2
August 2018
Pages 139-152

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History

  • Receive Date: 21 June 2016
  • Revise Date: 18 August 2016
  • Accept Date: 20 October 2018

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