FFT-PCA Image Fusion Based Flora and Vegetation Mapping Of Eshkevarat No Hunting Zone

fusion of remote sensing data is essential in order to obtain more information from different images. Mapping the vegetation of an area is very important due to its environmental importance. In this research, used Landsat ETM+ images and field surveying to identify vegetation states of the Eshkevarat No hunting zone. After applying necessary preprocessing like gap filling and atmospheric correction, the panchromatic and multi-spectral images were fused based on the FFT-PCA algorithm. In the next section, the fused image was classified based on the Support Vector Machine (SVM), algorithm into five classes. The results showed that the overall accuracy and kappa coefficient of classified images is 0.943% and 0.910 respectively. In order to field surveying of study area, 1-meter plots in 500-meter distance choose and 14 Flora and vegetation species were identified and mapped. The results showed that satellite images have good accuracy in this field but based on its spatial resolution limitations a large number of species present in the area have not been identified. In this research, it is suggested to use a combination of both satellite image sources and field surveys.