Remote sensing is one of the effective tools to study the process of land use change on a large scale and in a short time. In this research, the aim is to monitor and analyze land use changes using satellite images and remote sensing from 2010 to 2020 in Sabzevar city with Landsat images. For research, preprocessing included atmospheric correction and radiometric and geometric correction. A total of 200 ground control points were collected to classify and evaluate the accuracy of the classification with the maximum probability classification algorithm in the ground visit. The classification results showed that the forest area in 2010 was equal to 68980.21 hectares, which with the change of use and its conversion to residential use, barren and rainfed agriculture in 2020 reached 66044.99 hectares, ie 2935.22 hectares, its area has decreased. Residential use with its growth in 2010 to 2020 has increased from 2855.89 to 4563.98, ie 1708.09 hectares. Land use changes in semi-dense rangeland have also decreased from 167164.89 to 153287.68 hectares, i.e. 13877.21. Kappa coefficient and overall accuracy in 2020 were 98.42 and 97.84, respectively, which was the highest value compared to previous years. In this study, it can be recommended that the government increase the vegetation of the land to protect pasture and forest uses against further changes, and to compensate for these changes, to plant fast-growing forests. تفاصيل المقالة

Investigation and analysis of land use changes was done using remote sensing and GIS techniques with supervised classification methods. The selected images from the years 2000 and 2022 were taken by the Landsat satellite. Necessary pre-processing of the images was done and then the best band combination was selected. The best band combinations of 2000 and 2022 were selected as 245 and 467, respectively, using the OIF index. The area changes from 2000 to 2022, in the support vector machine method, the uses of dense pasture, poor pasture, agriculture, residential, forest have had area changes of 9580.53, 34267.49, 237.2, 1603.41, 26527.57 hectares. Therefore, the use of dense pasture and forest has decreased by 5.87% and 16.25%, and other uses have increased. The area changes from 2000 to 2022, in the neural network method, the uses of dense pasture, poor pasture, agriculture, residential, forest have had area changes of 6021.05, 33869.57, 360.79, 1492.16, 29701.47 hectares. Therefore, the use of dense pasture and forest has decreased by 3.69% and 18.20%, and the use of poor pasture has increased by 20.75%, agriculture by 0.22%, and residential by 0.91%. In the assessment of classification accuracy, kappa coefficient and overall accuracy in the support vector machine method in 2000 were 0.84 and 0.87 and in 2022, 0.86 and 0.88 were obtained. Kappa coefficient and overall accuracy were obtained in 2000, 0.94 and 0.95 and in 2022, 0.96 and 0.97 in the neural network method. Therefore, the neural network method has higher accuracy. تفاصيل المقالة