Separating vegetation types using LISS III, IRS-P6 satellite

Today, using satellite data because of excellent abilities in evaluating vegetation and preparing forest map and different lands map has high importance in ecological researches. This study was carried out in order to study of vegetation types in Galoochar Juniper Forest reservoir using IRS-P6, LISS III Satellite in Rabor town in the southeast of Iran. First, the bands were controlled according to radiometric and geometric errors. No radiometric distortion was found, Geometric correction was performed by 18 ground control points with digital elevation model, up to orthorectification level with precision of less than half pixel (RMSE=0.35 pixel). Ground truth map was prepared through sampling in 17% of whole area. It was used in order to evaluate the correct conclusion of classification of image. The supervised classification was performed by using basic and synthetic bands to 7 classes. Two supervised classification methods, Maximum Likelihood and Minimum Distance to Means were applied to classify the digital data in present study. The results showed that the highest overall accuracy belongs to Maximum Likelihood classification for 7 classes which was 65.38% and kappa coefficient was 0.6174. It can be indicated that using LISS III data is appropriate in the forest structure studies.