The distinction between barren and build-up areas is one of the most important issues in land use/land cover mapping ‎in arid and semi-arid climates. In this regard, many researchers have tried to increase the accuracy of classification ‎using different methods
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The distinction between barren and build-up areas is one of the most important issues in land use/land cover mapping ‎in arid and semi-arid climates. In this regard, many researchers have tried to increase the accuracy of classification ‎using different methods that, some of which are complex and time-consuming. Therefore, the present study conducted ‎aimed to apply micro-climate change through the implementation of Local Climate Zoning (LCZ) algorithm in land ‎use identification with emphasis on the separation of build-up areas in one of the arid cities of Iran, and the efficiency ‎of the method by investigation the classification accuracy was compared with various supervised methods including ‎maximum likelihood, minimum distance, Fisher, KNN, fuzzy, artificial neural network and support vector machine. ‎The study area is Zahedan city, which has a very significant growth of build-up areas in recent decades. For this ‎purpose, four periods of Landsat satellite images year 2020 were used. Training samples were extracted from Google ‎Earth and the validation of the classification results was performed using 218 random points. The accuracy results ‎showed that the use of LCZ algorithm with overall accuracy and kappa coefficient of 96.33% and 0.95, respectively is ‎the highest and then the support vector machine and Fisher methods with overall accuracy of 86.61 and 83.03 and ‎kappa coefficient of 0.82 and 0.75, respectively. Therefore, for land use / land cover studies, the LCZ method that ‎considers the micro-climate, is proposed.‎
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