An investigation on the suitability of ETM+ imagery for discrimination of land units of coastal area

Abstract
   In order to study the capability of ETM+ imagery for discrimination of land units of coastal areas, apportion of Khalidj-o-Omani district was selected. For these studies ETM+ images of Landsat 7 of April 15, 2000 were used. The radiometric and geometric errors of images were investigated and they were not so significant for this study. The scanned topographic map   (scale = 1:50000) were georeferenced using corners georeference technique, and then they were used for georeferencing the images by tiepoints georeference technique. Also the reference points were checked in the field by GPS. Finally the results showed 0.31 pixel accuracy of RMSE.
   In this study the aerial photographs of the study area were visually interpreted and checked by field study. Finally a digital map was produced (scale = 1:50000). In the next blush, analyzing of map were performed in the Geographical Information System (GIS) and the final phenomena (classes) were separated to use as the ground truth for assessment of classified color composites. Principal Component Analysis (PCA), rationing, Linear stretch, and density slicing were applied to ETM+ images. During field study, training samples for classifying were recognized. In addition more training samples were introduced based on the data developed from aerial photographs.
   The different band combinations were classified by maximum likelihood classification method. Optimum Index Factor (OIF) was used to select the best color composites. The statistical parameters of training samples including: average, standard deviation, and also overlapping of samples were studied. Three final classes were mapped, including: Avicina marina, Halocnemun strobilaceum, and Sea.
   Maximum likelihood classification was used in this study. The best color composite was IPVI, IR2, and PC1 with overall accuracy and Kappa coefficient 90% and 88.5% respectively. Final results showed that ETM+ data can be used to discriminate the coastal phenomena located in the Khalidj-o-Omani area.
Key words: Landsat 7, ETM+ Images, Bushehr, Khalidj-o-Omanian region, Asaloyeh, Nayband,Saline Area, Halophyte Plants, Remote Sensing, Maximum likelihood,  Mangrove Forests.