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 landuse maps from six supervised classification algorithms. As a result, different algorithms such as the minimum distance ofmean (MDM), Mahalanobis distance (MD), maximum likelihood (ML), artificial neural network (ANN), spectral anglemapper (SAM), and support vector machine (SVM) were considered in three areas of Iran's dry climate. The selectedstudy areas for dry climates were Shahreza, Taft and Zarand in Isfahan, Yazd, and Kerman Provinces, respectively. ThreeLandsat ETM+ images and topographical maps of 1:25,000-scale were used in the present study. In addition, trainingsamples for each land use were constructed using GPS and extensive field surveys. The training sites were divided intotwo 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 averagesof 0.9409 and 0.9315 Kappa coefficients are the best algorithms for land use mapping. The ANOVA test was performed onKappa coefficients, and the result shows that there are significant differences at the 1% level, between the differentalgorithms for the dry climate zones. These results can be used for land use planning, as well as environmental and naturalresources purposes in study areas.