Detection of land use changes using satellite imagery during the period 1984-2019 (Case study of Kamyaran city)

Land use change due to human activities is one of the important issues in regional and development planning. Given the advantages and capabilities of satellite data, this technology can be of great help in identifying and detecting these changes. The purpose of this study is to detect land use changes in Kamyaran city using satellite images over a period of 35 years. In this study, data from 1984 TM sensor, 2000 ETM + sensor and 2019 Landsat OLI sensor were used.  Initially, preliminary preprocessions including radiometric, atmospheric and geometric corrections were performed on the raw data. Land control points were used for training, accreditation and to prepare land use map. Land use class was determined by field survey and using Google Earth images in 9 land use classes of agricultural lands, forests, gardens, rich and wooded pastures, medium rangelands, residential areas, water area, barren lands and rock outcrops. Next, the neural network method was used to monitor the images in ENVI 5.3 software. The evaluation results showed that the overall accuracy and kappa coefficient of OLI classified images are 94.3 and 0.92%, ETM + 92.6 and 0.91% and TM 90.3 and 0.87%, respectively. The results showed that forest lands and rich and wooded pastures decreased significantly during three time periods, which decreased by 11.64 and 19.12 percent, respectively. So that rich and wooded pastures have an increasing trend until 2000 and in the next period until 2019 has a decreasing trend. Residential lands, water areas and gardens increased by 2.27%, 0.57% and 3.98%, respectively. Due to the growing trend of population and urbanization, the results of this study provide the necessary information to make basic decisions in the development of management policies for planners and regional managers for the sustainability and evaluation of natural resources.