Quality of life in urban environments is closely related to vegetation cover. The Urban growth and its related environmental problems, planners are forced to implement policies to improve the quality of urban environment. Thus, vegetation mapping for planning and managing urban is critical. Given the spectral complexity of the urban environment and the sparse vegetation in these areas, to generate a reliable map of coverage Vegetation in these areas requires the use of high spatial resolution images. But given the size of cities and the rapid changes in vegetation status, Mapping of vegetation using these images will have cost much. In this study, using a moderate spatial resolution image with the help of a small part of high spatial resolution image vegetation cover in a Metropolitan area is obtained. We make use of Ikonos image to get Fractional vegetation cover (FVC) and used as a vicarious validation of FVC. Then using linear and nonlinear regression and neural network between the FVC derived from the Ikonos image and vegetation indices on Landsat image, the relationship was established. A number of pixels were randomly selected from the images for the model validation. The results show that the neural network, nonlinear regression and linear regression models are more accurate for the estimation of FVC respectively.