Remote sensing-assisted mapping of quantitative attributes in Zagros open forests of Iran

The Zagros forests come as one of the most valuable ecosystems in western Iran. Therefore, accurate and up-to-date information on basal area, canopy cover, and stem number per hectare of these forests are the important factors in the context of forest management and conservation. The main objective of this study was to estimate quantitative forest attributes using Landsat 8-OLI image data and Random Forest, a well-known machine learning technique. The results were shown the lowest out of bag error with the combination of 800 trees and 8 variables in each node as the optimal model parameters to classify forest canopy cover with overall accuracy and Kappa coefficient of 83% and 0.73 respectively, while those of classified mapping of basal area were 78% and 0.72, and also those of stem number per hectare were 75% and 0.69 respectively. All in all, the Random Forest classifier algorithm provided comparatively successful mapping results of quantitative attributes in Zagros open forests of Iran from Landsat 8-OLI image data.