Forest Stand Age Classification Using Landsat ETM+ Data

Classifying age classes in a large area using remotely sensed data has considerable significance for forest sustainable management. In this research, Landsat ETM+ data from Loveh forest, dating July 2002, were analyzed to investigate the potential of this sensor for age class mapping. We applied a systematic cluster sampling method to collect field data. We used 99 plots so that contained 32 plot. In stands with 25-45 years, 33 plots in stand with 5-25 years and 34 plots in stands with >45 years. The quality of the image was first evaluated for radiometric noises. Separability of three age classes 5-25, 25-45 and >45 years, using a supervised classification and four algorithm of maximum likelihood, minimum distance, parallel piped and linear discriminate analysis (Fisher). The results showed that maximum likelihood in three and two age classes with overall accuracy and kappa coefficient were (79% and 94%) and (0.68 and 0.86), respectively. Signature separability, producer and user accuracies showed the highest spectral similarity between 5-25 and >45 age classes. By merging the two classes, the overall accuracy and kappa coefficient became equal to 94% and 0.86, respectively. These results demonstrate that the reflectance values recorded by ETM+ sensor are related to forest stands. This information could also be used to estimate forest biomass and carbon content, identify locations within the stands that might require treatment and plan other management activities.