Monitoring the status of Bakhtegan Lake and surrounding areas using satellite imagery and computational intelligence

Multilispectral picture classification is one of the most important techniques for separating earth units.The phenomenon of global warming,expansion damming,water storage behind dams and excessive utilization of existing water for human uses has caused the drying of lakes, including Lake Bakhtegan. For this purpose, Landsat images of 1991, 2000, 2010, and 2017 were collected in Bakhtegan Lake and surrounding areas. These images were categorized based on educational samples in four classes of water, septicity, mountain and urban areas after pre-processing and corrections required by the supervised maximum likeness.The same image was then sorted by multi-layer perceptron neural network method in the above classes. Finally, for both methods, the error matrix was extracted and the overall accuracy and kappa coefficient were calculated.For the year 1991, the maximum probability and neural network method was 87% and 93%, and the kappa coefficient was calculated to be 0.86 and 0.90, respectively. . Therefore, due to the higher accuracy of Negative Network, images of the years 2000, 2010 and 2017 were categorized by this method.After classification, in order to evaluate it, Google Earth was considered as the test sample for each information class and the overall accuracy and kappa coefficient were 89% and 0.85, respectively.