عنوان مقاله [English]
This research focused on determining the possibility of separating different percents of Beech and Hornbeam species based on two methods of spectral reflectance assessment and classification using landsat imagery in a northern forest of Iran. To do this, Landsat7 ETM+ imagery of the study area for 24 June, 2000 (middle of germination season) were provided. The 100×100 m (1 ha) sample plots with 90m distances from each other over 3 areas containing Beach and Hornbeam mixtures were transmitted from forest type map on a 1:25000 scale. Four groups were determined after field inventory, I) pure Beech, II) 80% Beech and 20% Hornbeam, III) 70% Beech and 30% Hornbeam and IV) 60% Beech and 40% Hornbeam. To avoid road reflectance interference with tree reflectance, sample plots were selected with 60 m distance from roads. Followed by digitizing the roads and plots, the images were geometrically corrected at the ortho level using 13 ground control points. Then, the digitized roads and sample plots were overlaid on 6 bands (1, 2, 3, 4, 5 and 7) of the ETM+ imagery and their pixel data were extracted. In the next step, the obtained data was statistically analyzed. The differences among the selected groups were compared via t-test method. The results showed the capability of ETM+ imagery in separating the pure Beech from the mixture of 70% Beech and 30% Hornbeam and 60% Beech and 40% Hornbeam. Then, the images were classified using a Maximum Likelihood Algorithm. Based on the classification results, the maximum likelihood were measured with 63% total accuracy and 44% kappa coefficient. The results of comparing four above mentioned groups using maximum likelihood classification method were in accordance with the results of applying their pixel values.