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Identifying the accurate time and use changes in land involves a better understanding of the human interactions and impacts on land resources. Detecting the changes is a process that allows observing and recognizing the differences and time series differences of phenomena, complications, and patterns of the earth’s surface. The purpose of this study is to investigate the land use change trends in Yazd-Ardakan plain using remote sensing method from 1986 to 2016 and Landsat satellite images and emphasizing the object-oriented classification method. In this research, Landsat satellite images (MSS) for 1986, Landsat (TM) for 1999 and 2010, and Landsat 8 (OLI Sensor) for 2016 were used. To illustrate the classification outcomes well, some indicators such as Principal Components Analysis (PCA), Normalized Difference Vegetation Index (NDVI), and Crosstab monitoring were used. The results of classification using the object-oriented method in 1999, had the highest accuracy in classifying land users with the total accuracy and kappa coefficients of 86 and 94, in Yazd-Ardakan plain, respectively. The results showed that during the period of 1986-2016, the area of agriculture lands and poor pasture area were decreased by 5696 and 579888 ha (-1.18% and 12.01%) respectively, while the barren, residential and the sand dunes were exposed to an increasing trend of 2419, 35454 and 457 ha (5.16, 7.34 and 0.09) respectively. In other words, it can be stated that in this period of 30 years, the most changes were related to poor pasture and residential lands. The trend of utilization changes indicates the general trend of destruction in the region through the replacement of agriculture land and poor pasture by land utilizations and residential areas as well as sand dunes. Reducing the extent of poor rangeland coverage and increasing trend of other uses indicate a general deterioration in the region and the replacement of weaker uses in the area, which show an increase in population and an increase in human pressure in the studied area. (C) 2018 Elsevier Ltd. All rights reserved.