Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran

The objective of this research was to determine the best model and compare performances in terms of producing land use maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance of mean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral angle mapper (SAM), and support vector machine (SVM) were considered in three areas of Iran's dry climate. The selected study areas for dry climates were Shahreza, Taft and Zarand in Isfahan, Yazd, and Kerman Provinces, respectively. Three Landsat ETM+ images and topographical maps of 1:25,000-scale were used in the present study. In addition, training samples for each land use were constructed using GPS and extensive field surveys. The training sites were divided into two categories; one category was used for image classification and the other for classification accuracy assessment. Results show that for the dry climate areas, Maximum Likelihood and Support Vector Machine algorithms with averages of 0.9409 and 0.9315 Kappa coefficients are the best algorithms for land use mapping. The ANOVA test was performed on Kappa coefficients, and the result shows that there are significant differences at the 1% level, between the different algorithms for the dry climate zones. These results can be used for land use planning, as well as environmental and natural resources purposes in study areas.