Investigating Capability of OLI Data of Landsat 8 for Estimation of Canopy Density in Zagros Forests

In this study for evaluation capability, OLI data of Landsat8 to estimate canopy density 2300 ha. in protected Manesht area in Zagros forests of Iran was selected. For ground truth data, 100 square plots (0.36 ha) were measured and systematic random sampling method was used. The dimensions of network inventory were 500m×400m. In each plot, crown cover was measured and then canopy percent in each plot was calculated. For classification and mapping, maximum likelihood and minimum distance to mean classifiers were used. The Transformed Divergence index was used to determine best combination of image bands. Result of this study showed that minimum distance to mean classifier had overall accuracy and kappa coefficient of 80% and 0.68 respectively on OLI image data. In addition, the maximum likelihood classifier had overall accuracy and kappa coefficient of 60% and 0.35 respectively. The result of this study showed that minimum distance to mean classifier was most suitable classifier for canopy classification of Zagros forests on the OLI image data. Keywords: Ilam, Landsat8, OLI sensor, Zagros forest.